@conference{GEMA-congress_2024, author = "Manuel L. Rodr{\'i}guez Per{\'a}lvarez and Gloria de la Rosa and Antonio Manuel G{\'o}mez-Orellana and Mª Victoria Aguilera and Teresa Pascual Vicente and Sheila Pereira and Mar{\'i}a Luisa Ortiz and Giulia Pagano and Francisco Suarez and Roc{\'i}o Gonz{\'a}lez Grande and Alba Cachero and Santiago Tom{\'e} and M{\'o}nica Barreales and Rosa Mart{\'i}n Mateos and Sonia Pascual and Mario Romero and Itxarone Bilbao and Carmen Alonson Mart{\'i}n and Elena Ot{\'o}n and Luisa Gonz{\'a}lez Di{\'e}guez and Mar{\'i}a Dolores Espinosa and Ana Arias Milla and Gerardo Blanco Fern{\'a}ndez and Sara Lorente and Antonio Cuadrado Lav{\'i}n and Amaya Red{\'i}n Garc{\'i}a and Clara S{\'a}nchez Cano and Carmen Cepeda and Jos{\'e} Antonio Pons and Jordi Colmenero and Alejandra Otero and Nerea Hern{\'a}ndez Aretxabaleta and Sarai Romero Moreno and Mar{\'i}a Rodr{\'i}guez Soler and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Mikel Gastaca", booktitle = "48.º Congreso Anual de la Asociaci{\'o}n Espa{\~n}ola para el Estudio del H{\'i}gado", title = "{UTILIDAD} {DEL} {GENDER}-{EQUITY} {MODEL} {FOR} {LIVER} {ALLOCATION} ({GEMA}) {EN} {UN} {CONTEXTO} {DE} {ACORTAMIENTO} {DE} {LA} {LISTA} {DE} {ESPERA} {DE} {TRASPLANTE} {HEP}{\'{A}}{TICO}", year = "2024", } @conference{BarycentreAveragingTimeSeries_2023, author = "Christopher Holder and David Guijo-Rubio and Anthony Bagnall", abstract = "Distance functions play a core role in many time series machine learning algorithms for tasks such as clustering, classification and regression. Time series often require bespoke distance functions because small offsets in time can lead to large distances between series that are conceptually similar. Elastic distances compensate for misalignment by creating a path through a cost matrix by warping and/or editing time series. Time series are most commonly clustered with partitional algorithms such as k-means and k-medoids using elastic distance measures such as Dynamic Time Warping (DTW). The distance is used to assign cases to the closest cluster representative. k-means requires the averaging of time series to find these representative centroids. If DTW is used to assign membership, but the arithmetic mean is used to find centroids, k-means performance degrades significantly. An averaging technique specific to DTW, called DTW Barycentre Averaging (DBA), overcomes the averaging problem however, can only be used with DTW. As such alternative distance functions such as Move-Split-Merge (MSM) are forced to use the arithmetic mean to compute new centroids and suffer similar degraded performance as k-means-DTW without DBA. To address this we propose a averaging method for MSM distance, MSM Barycentre Averaging (MBA) and show that when used to find centroids it significantly improves MSM based k-means and is better than commonly used alternatives.", booktitle = "15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management", doi = "https://www.scitepress.org/PublicationsDetail.aspx?ID=h27zf/TeD4k={\&}t=1", keywords = "Time Series Distances, Time Series Clustering, Move Split Merge, Barycentre Averaging, Dynamic Barycentre Averaging, MSM Barycentre Averaging, DBA, MBA", pages = "51--62", title = " {B}arycentre {A}veraging for the {M}ove-{S}plit-{M}erge {T}ime {S}eries {D}istance {M}easure ", url = "www.scitepress.org/PublicationsDetail.aspx?ID=h27zf/TeD4k={\&}t=1", volume = "1", year = "2023", } @conference{ClusteringTimeSeries_kMedoids_2023, author = "Christopher Holder and David Guijo-Rubio and Anthony Bagnall", abstract = "Time Series Clustering (TSCL) involves grouping unlabelled time series into homogeneous groups. A popular approach to TSCL is to use the partitional clustering algorithms k-means or k-medoids in conjunction with an elastic distance function such as Dynamic Time Warping (DTW). We explore TSCL using nine different elastic distance measures. Both partitional algorithms characterise clusters with an exemplar series, but use different techniques to do so: k-means uses an averaging algorithm to find an exemplar, whereas k-medoids chooses a training case (medoid). Traditionally, the arithmetic mean of a collection of time series was used with k-means. However, this ignores any offset. In 2011, an averaging technique specific to DTW, called DTW Barycentre Averaging (DBA), was proposed. Since, k-means with DBA has been the algorithm of choice for the majority of partition-based TSCL and much of the research using medoids-based approaches for TSCL stopped. We revisit k-medoids based TSCL with a range of elastic distance measures. Our results show k-medoids approaches are significantly better than k-means on a standard test suite, independent of the elastic distance measure used. We also compare the most commonly used alternating k-medoids approach against the Partition Around Medoids (PAM) algorithm. PAM significantly outperforms the default k-medoids for all nine elastic measures used. Additionally, we evaluate six variants of PAM designed to speed up TSCL. Finally, we show PAM with the best elastic distance measure is significantly better than popular alternative TSCL algorithms, including the k-means DBA approach, and competitive with the best deep learning algorithms.", booktitle = "Advanced Analytics and Learning on Temporal Data", doi = "10.1007/978-3-031-49896-1_4", keywords = "Time Series, Clustering, k-means, k-medoids, PAM, UCR archive", pages = "39--55", title = "{C}lustering {T}ime {S}eries with k-{M}edoids {B}ased {A}lgorithms", url = "link.springer.com/chapter/10.1007/978-3-031-49896-1_4", year = "2023", } @conference{GEMA-Na-Congress-2023, author = "M. Rodr{\'i}guez-Per{\'a}lvarez and Antonio Manuel G{\'o}mez-Orellana and A. Majumdar and M. Bailey and G. McCaughan and P. Gow and M. Guerrero and R. Taylor and David Guijo-Rubio and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and E. Tsochatzis", abstract = "Models for liver transplant (LT) allocation have been trained to predict mortality or delisting for sickness at 90 days. Their performance in a context of waiting list shortening is uncertain.", booktitle = "ILTS 2023 Joint International Congress of ILTS, ELITA and LICAGE, May 3-6, 2023.", doi = "https://doi.org/10.1097/01.tp.0000978836.44371.fe", organization = "Transplantation", title = "{P}erformance of the gender-equity model for liver allocation ({GEMA}-{N}a) within the first 30 and 60 days of listing", url = "doi.org/10.1097/01.tp.0000978836.44371.fe", year = "2023", } @conference{GEMA-congress-2023, author = "Manuel Luis Rodr{\'i}guez Per{\'a}lvarez and Antonio Manuel G{\'o}mez-Orellana and Avik Majumdar and Mar{\'i}a Dolores Ayll{\'o}n and Pedro Antonio Guti{\'e}rrez and Pilar Barrera Baena and David Guijo-Rubio and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Manuel de la Mata and Emmanuel Tsochatzis", abstract = "Los modelos de priorizaci{\'o}n en lista de espera de trasplante hep{\'a}tico (TH) han sido entrenados para predecir mortalidad en lista a 90 d{\'i}as. Sin embargo, muchos centros tienen listas de espera m{\'a}s cortas, lo cual podr{\'i}a cuestionar su utilidad.", booktitle = "49.º Congreso Anual de la Asociaci{\'o}n Espa{\~n}ola para el Estudio del H{\'i}gado", doi = "https://www.elsevier.es/es-revista-gastroenterologia-hepatologia-14-sumario-vol-46-num-s2-X0210570523X00S20?local=true", title = "{UTILIDAD} {DEL} {GENDER}-{EQUITY} {MODEL} {FOR} {LIVER} {ALLOCATION} ({GEMA}) {EN} {UN} {CONTEXTO} {DE} {ACORTAMIENTO} {DE} {LA} {LISTA} {DE} {ESPERA} {DE} {TRASPLANTE} {HEP}{\'{A}}{TICO}", url = "www.elsevier.es/es-revista-gastroenterologia-hepatologia-14-sumario-vol-46-num-s2-X0210570523X00S20?local=true", year = "2023", } @conference{Dictionary-Based_TimeSeries_2023, author = "Rafael Ayll{\'o}n-Gavil{\'a}n and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Time Series Classification (TSC) is an extensively researched field from which a broad range of real-world problems can be addressed obtaining excellent results. One sort of the approaches performing well are the so-called dictionary-based techniques. The Temporal Dictionary Ensemble (TDE) is the current state-of-the-art dictionary-based TSC approach. In many TSC problems we find a natural ordering in the labels associated with the time series. This characteristic is referred to as ordinality, and can be exploited to improve the methods performance. The area dealing with ordinal time series is the Time Series Ordinal Classification (TSOC) field, which is yet unexplored. In this work, we present an ordinal adaptation of the TDE algorithm, known as ordinal TDE (O-TDE). For this, a comprehensive comparison using a set of 18 TSOC problems is performed. Experiments conducted show the improvement achieved by the ordinal dictionary-based approach in comparison to four other existing nominal dictionary-based techniques.", booktitle = "IWANN 2023: Advances in Computational Intelligence", doi = "10.1007/978-3-031-43078-7_44", keywords = "Time Series, Dictionary-Based Approaches, Ordinal Classification", number = "1", pages = "1--12", title = "{A} {D}ictionary-{B}ased {A}pproach to {T}ime {S}eries {O}rdinal {C}lassification", url = "link.springer.com/chapter/10.1007/978-3-031-43078-7_44", year = "2023", } @conference{EvaluatingPerformance_ExplanationMethods_2023, author = "Javier Barbero-G{\'o}mez and Ricardo Cruz and Jaime S. Cardoso and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper introduces an evaluation procedure to validate the efficacy of explanation methods for Convolutional Neural Network (CNN) models in ordinal regression tasks. Two ordinal methods are contrasted against a baseline using cross-entropy, across four datasets. A statistical analysis demonstrates that attribution methods, such as Grad-CAM and IBA, perform significantly better when used with ordinal regression CNN models compared to a baseline approach in most ordinal and nominal metrics. The study suggests that incorporating ordinal information into the attribution map construction process may improve the explanations further.", booktitle = "IWANN 2023: Advances in Computational Intelligence", doi = "10.1007/978-3-031-43078-7_43", keywords = "Convolutional Neural Networks, Interpretability, Ordinal Regression", number = "1", pages = "1--12", title = "{E}valuating the {P}erformance of {E}xplanation {M}ethods on {O}rdinal {R}egression {CNN} {M}odels", url = "link.springer.com/chapter/10.1007/978-3-031-43078-7_43", year = "2023", } @conference{OrdinalClassification_DR_2023, author = "M. Rivera-Gavil{\'a}n and V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and J. Brice{\~n}o and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and David Guijo-Rubio", abstract = "This paper tackles the Donor-Recipient (D-R) matching for Liver Transplantation (LT). Typically, D-R matching is performed following the knowledge of a team of experts guided by the use of a prioritisation system. One of the most extended, the Model for End-stage Liver Disease (MELD), aims to decrease the mortality in the waiting list. However, it does not take into account the result of the transplant. In this sense, with the aim of developing a system able to bear in mind the survival benefit, we propose to treat the problem as an ordinal classification one. The organ survival will be predicted at four different thresholds. The results achieved demonstrate that ordinal classifiers are capable of outperforming nominal approaches in the state-of-the-art. Finally, this methodology can help experts to make more informed decisions about the appropriateness of assigning a recipient for a specific donor, maximising the probability of post-transplant survival in LT.", booktitle = "IWANN 2023: Advances in Computational Intelligence", doi = "10.1007/978-3-031-43078-7_42", keywords = "Donor-Recipient Matching, Liver Transplantation, Ordinal Classification, Ordinal Binary Decomposition", number = "1", pages = "1--12", title = "{O}rdinal classification approach for donor-recipient matching in liver transplantation with circulatory death donors", url = "link.springer.com/chapter/10.1007/978-3-031-43078-7_42", year = "2023", } @conference{GramarianAngular_iwan_2023, author = "V{\'i}ctor Manuel Vargas and Rafael Ayll{\'o}n-Gavil{\'a}n and Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and David Guijo-Rubio", abstract = "This work presents a novel ordinal Deep Learning (DL) approach to Time Series Ordinal Classification (TSOC) field. TSOC consists in classifying time series with labels showing a natural order between them. This particular property of the output variable should be exploited to boost the performance for a given problem. This paper presents a novel DL approach in which time series are encoded as 3-channels images using Gramian Angular Field and Markov Transition Field. A soft labelling approach, which considers the probabilities generated by a unimodal distribution for obtaining soft labels that replace crisp labels in the loss function, is applied to a ResNet18 model. Specifically, beta and triangular distributions have been applied. They have been compared against three state-of-the-art deep learners in the Time Series Classification (TSC) field using 13 univariate and multivariate time series datasets. The approach considering the triangular distribution (O-GAMTFT) outperforms all the techniques benchmarked.", booktitle = "IWANN 2023: Advances in Computational Intelligence", doi = "10.1007/978-3-031-43078-7_41", keywords = "Gramarian Angular Fields, Markov Transition Fields, Time Series Ordinal Classification, Soft Labelling", number = "1", pages = "1--12", title = "{G}ramian {A}ngular and {M}arkov {T}ransition {F}ields applied to {T}ime {S}eries {O}rdinal {C}lassification", url = "link.springer.com/chapter/10.1007/978-3-031-43078-7_41", year = "2023", } @conference{R. Calleja2023, author = "R. Calleja and Marcos Rivera and Amelia J. Hessheimer and Beatriz Dom{\'i}nguez-Gil and David Guijo-Rubio and Constantino Fontdevila and Mikel Gastaca-Mateo and Manuel C{\'o}mez and Pablo Ram{\'i}rez-Romero and Rafael L{\'o}pez-Andujar and L{\'a}nder Atutxa and Julio Santoyo and Miguel A. G{\'o}mez-Bravo and Jes{\'u}s M. Villar-del-Moral and Carolina Gonz{\'a}lez-Abos and B{\'a}rbara Vidal and Laura Llad{\'o} and Jos{\'e} Rold{\'a}n and Carlos Jim{\'e}nez-Romero and V{\'i}ctor S{\'a}nchez-Turri{\'o}n and Gonzalo Rodr{\'i}guez-Laiz and Jos{\'e} A. L{\'o}pez Baena and Ram{\'o}n Charco-Torra and Evaristo Varo and Fernando Rotellar and Manuel Barrera and Juan C. Rodr{\'i}guez and Gerardo Blanco-Fern{\'a}ndez and Javier Nu{\~n}o and David Pacheco S{\'a}nchez and Elisabeth Coll and Gloria de la Rosa and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Javier Brice{\~n}o", booktitle = "29o Congreso de la Sociedad Espa{\~n}ola de Trasplante Hep{\'a}tico (SETH)", month = "Noviembre", pages = "9", title = "{E}mparejamiento donante-receptor durante la donaci{\'o}n en asistolia controlada con perfusi{\'o}n regional normot{\'e}rmica: papel de los clasificadores de machine learning como modelos predictivos de la supervivencia del injerto", url = "https://sethepatico.org/seth2023/presentaciones/index.htm", year = "2023", } @conference{262023, author = "Antonio Manuel G{\'o}mez-Orellana and Manuel L. Rodr{\'i}guez-Per{\'a}lvarez and David Guijo-Rubio and Marta Guerrero and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", booktitle = "Ciencia Violeta. I Encuentro Cient{\'i}fico sobre Investigaci{\'o}n con Perspectiva de G{\'e}nero", month = "Febrero", pages = "1-5", title = "{C}orrecci{\'o}n de la disparidad de g{\'e}nero en el acceso al trasplante hep{\'a}tico", year = "2023", } @conference{VictorVargasATM-2022, author = "Riccardo Rosati and Luca Romeo and V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Lorenzo Bianchini and Alessandra Capriotti and Rosario Capparuccia and Emanuele Frontoni", abstract = "One of the main relevant topics of Industry 4.0 is related to the prediction of Remaining Useful Life (RUL) of machines. In this context, the Smart Manufacturing Machine with Predictive Lifetime Electronic maintenance (SIMPLE) project aims to promote collaborations among different companies in the scenario of predictive maintenance. One of the topics of the SIMPLE project is related to the prediction of RUL of automated teller machines (ATMs). This represents a key task as these machines are subject to different types of failure. However the main challenges in this field lie in: i) collecting a representative dataset, ii) correctly annotating the observations and iii) handling the imbalanced nature of the dataset. To overcome this problem, in this work we present a feature extraction strategy and a machine learning (ML) based solution for solving RUL estimation for ATM devices. We prove the effectiveness of our approach with respect to other state-of-the-art ML approaches widely employed for solving the RUL task. In addition, we propose the design of a predictive maintenance platform to integrate our ML model for the SIMPLE project.", booktitle = "Proceedings of the 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022)", doi = "10.1007/978-3-031-18050-7_23", isbn = "978-3-031-18049-1", month = "September", organization = "Salamanca, Spain", pages = "239--249", series = "Lecture Notes in Networks and Systems", title = "{P}redictive {M}aintenance of {ATM} machines by modelling {R}emaining {U}seful {L}ife with {M}achine {L}earning techniques", url = "doi.org/10.1007/978-3-031-18050-7_23", volume = "531", year = "2022", } @conference{AyllonGavilan2022, author = "Rafael Ayll{\'o}n-Gavil{\'a}n and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This work analyzes the performance of several state-of-the-art Time Series Classification (TSC) techniques in the cryptocurrency returns modeling field. The data used in this study comprehends the close price of $6$ of the principal cryptocurrencies, collected with a frequency of $5$ minutes from January $1$st to September $21$th of $2021$. The aim of this work is twofold: 1) to study the weak form of the Efficient Market Hypothesis (EMH) and 2) to examine the veracity behind the theory of the Random Walk Model (RWM). For this, two datasets are built. The first uses autoregressive values, whereas the second dataset is constructed by introducing randomized past values from the time series. Then, a comparison of the performances achieved by the different TSC techniques is carried out. Results obtained show a pronounced difference in terms of performance obtained by all the TSC models when applied to the original dataset against the randomized one. The results achieved by the models applied to the original dataset are significantly better in terms of Area Under ROC Curve (AUC) and Recall. Therefore, the EMH is refused in its weak form, and indisputable evidence against the RWM in a high-frequency scope is provided.", booktitle = "Proceedings of the 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022)", doi = "10.1007/978-3-031-18050-7_14", isbn = "978-3-031-18049-1", month = "September", organization = "Salamanca, Spain", pages = "146--155", series = "Lecture Notes in Networks and Systems", title = "{A}ssessing the {E}fficient {M}arket {H}ypothesis for {C}ryptocurrencies with {H}igh-{F}requency {D}ata using {T}ime {S}eries {C}lassification", url = "doi.org/10.1007/978-3-031-18050-7_14", volume = "531", year = "2022", } @conference{Tacrolimus_Liver_Transplantation_2022, author = "Manuel Rodr{\'i}guez-Per{\'a}lvez and Gonzalo Crespo and Jes{\'u}s Rivera and Antonio Gonz{\'a}lez Rodr{\'i}guez and Estefan{\'i}a Berge Garrido and Mikel Gastaca and Patricia Ruiz and Anna Curell and Cristina Dopazo and Ainhoa Fern{\'a}ndez-Yunquera and Fernando Diaz and Ana S{\'a}nchez Mart{\'i}nez and Mar{\'i}a Luisa Ortiz and Marina Berenguer and Tommaso Di Maira and Jose Ignacio Herrero and Mercedes I{\~n}arrairaegui and Carolina Almohalla and Esteban Fuentes Valenzuela and Sara Lorente Perez and Cristina Borao and Fernando Casafont and Emilio Fabrega and Sonia Pascual and Patricio M{\'a}s-Serrano and Maria Angeles Lopez Garrido and Flor Nogueras L{\'o}pez and Rocio Gonz{\'a}lez-Grande and Javier Zamora and Rafael Alejandre and Antonio Manuel G{\'o}mez-Orellana and Carmen Bernal and Miguel {\'A}ngel G{\'o}mez Bravo", awards = "JCR(2022): 25.7, Position: 4/93 (Q1) Category: GASTROENTEROLOGY {\&} HEPATOLOGY.", booktitle = "Abstracts of The International Liver CongressTM 2022", comments = "JCR(2022): 25.7, Position: 4/93 (Q1) Category: GASTROENTEROLOGY {\&} HEPATOLOGY.", doi = "10.1016/S0168-8278(22)00614-6", editor = "Journal of Hepatology", issn = "0168-8278", month = "June", note = "JCR(2022): 25.7, Position: 4/93 (Q1) Category: GASTROENTEROLOGY {\&} HEPATOLOGY.", organization = "London, United Kingdom", pages = "114", title = "{C}umulative exposure to tacrolimus and incidence of cancer after liver transplantation", url = "https://www.sciencedirect.com/science/article/pii/S0168827822006146", volume = "77", year = "2022", } @conference{GEMA_Liver_transplant_2022, author = "Manuel Rodr{\'i}guez-Per{\'a}lvarez and Antonio Manuel G{\'o}mez-Orellana and Avik Majumdar and Geoff McCaughan and Paul Gow and David Guijo-Rubio and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Michael Bailey and Emmanuel Tsochatzis", abstract = "The model for end stage liver disease (MELD) and its sodium-corrected variant (MELD-Na) have created gender disparities in accessing liver transplantation (LT). We derived and validated a new model that replaced creatinine with the Royal Free glomerular filtration rate (PMID: 27779785) within the MELD and MELD-Na formulas", awards = "JCR(2022): 25.7 Position: 4/93 (Q1) Category: GASTROENTEROLOGY {\&} HEPATOLOGY", booktitle = "Abstracts of The International Liver CongressTM 2022", comments = "JCR(2022): 25.7 Position: 4/93 (Q1) Category: GASTROENTEROLOGY {\&} HEPATOLOGY", doi = "10.1016/S0168-8278(22)00432-9", editor = "Journal of Hepatology", issn = "0168-8278", month = "June", note = "JCR(2022): 25.7 Position: 4/93 (Q1) Category: GASTROENTEROLOGY {\&} HEPATOLOGY", organization = "London, United Kingdom", pages = "3-4", title = "{D}evelopment and validation of the gender-equity model for liver allocation ({GEMA}) to prioritize liver transplant candidates", url = "www.sciencedirect.com/science/article/pii/S0168827822004329", volume = "77", year = "2022", } @conference{COVID19-MDiaz2022, author = "Miguel D{\'i}az-Lozano and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "In this paper, an approach based on a time series clustering technique is presented by extracting relevant features from the original temporal data. A curve characterization is applied to the daily contagion rates of the 34 sanitary districts of Andalusia, Spain. By determining the maximum incidence instant and two inflection points for each wave, an outbreak curve can be described by six intensity features, defining its initial and final phases. These features are used to derive different groups using state-of-the-art clustering techniques. The experimentation carried out indicates that {\$}{\$}k=3{\$}{\$}k=3is the optimum number of descriptive groups of intensities. According to the resulting clusters for each wave, the pandemic behavior in Andalusia can be visualised over time, showing the most affected districts in the pandemic period considered. Additionally, in order to perform a pandemic overview of the whole period, the approach is also applied to joint information of all the considered periods", booktitle = "Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence (Proceedings of the 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022)", doi = "10.1007/978-3-031-06527-9_46", isbn = "978-3-031-06527-9", keywords = "COVID-19 contagions, clustering, curve characterization", month = "May", number = "Part II", organization = "Tenerife, Spain", pages = "462--471", publisher = "Springer", series = "Lecture Notes in Computer Science (LNCS)", title = "{C}lustering of {COVID}-19 {T}ime {S}eries {I}ncidence {I}ntensity in {A}ndalusia, {S}pain", url = "doi.org/10.1007/978-3-031-06527-9_46", volume = "13259", year = "2022", } @conference{222022, author = "David Guijo-Rubio and V{\'i}ctor Manuel Vargas and Javier Barbero-G{\'o}mez and Jose V. Die and Pablo Gonz{\'a}lez-Moreno", abstract = "Programming has traditionally been an engineering competence, but recently it is acquiring significant importance in several areas, such as Life Sciences, where it is considered to be essential for problem solving based on data analysis. Therefore, students in these areas need to improve their programming skills related to the data analysis process. Similarly, engineering students with proven technical ability may lack the biological background which is likewise fundamental for problem-solving. Using hackathon and teamwork-based tools, students from both disciplines were challenged with a series of problems in the area of Life Sciences. To solve these problems, we established work teams that were trained before the beginning of the competition. Their results were assessed in relation to their approach in obtaining the data, performing the analysis and finally interpreting and presenting the results to solve the challenges. The project succeeded, meaning students solved the proposed problems and achieved the goals of the activity. This would have been difficult to address with teams made from the same field of study. The hackathon succeeded in generating a shared learning and a multidisciplinary experience for their professional training, being highly rewarding for both students and faculty members.", booktitle = "Proceedings of the 13th International Conference on European Transnational Education (ICEUTE 2022)", doi = "10.1007/978-3-031-18409-3_23", month = "5th - 7th September", pages = "236--246", publisher = "Springer", series = "Lecture Notes in Networks and Systems", title = "{H}ackathon in teaching: applying machine learning to {L}ife {S}ciences tasks", url = "doi.org/10.1007/978-3-031-18409-3_23", volume = "532", year = "2022", } @conference{182022, author = "Antonio Manuel Dur{\'a}n-Rosal and David Guijo-Rubio and V{\'i}ctor Manuel Vargas and Antonio Manuel G{\'o}mez-Orellana and Pedro Antonio Guti{\'e}rrez and Juan Carlos Fern{\'a}ndez", abstract = "Machine learning (ML) is the field of science that combines knowledge from artificial intelligence, statistics and mathematics intending to give computers the ability to learn from data without being explicitly programmed to do so. It falls under the umbrella of Data Science and is usually developed by Computer Engineers becoming what is known as Data Scientists. Developing the necessary competences in this field is not a trivial task, and applying innovative methodologies such as gamification can smooth the initial learning curve. In this context, communities offering platforms for open competitions such as Kaggle can be used as a motivating element. The main objective of this work is to gamify the classroom with the idea of providing students with valuable hands-on experience by means of addressing a real problem, as well as the possibility to cooperate and compete simultaneously to acquire ML competences. The innovative teaching experience carried out during two years meant a great motivation, an improvement of the learning capacity and a continuous recycling of knowledge to which Computer Engineers are faced to.", booktitle = "Joint Conference 15th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2022), 13th International Conference on EUropean Transnational Education (ICEUTE 2022)", doi = "10.1007/978-3-031-18409-3_22", isbn = "978-3-031-18409-3", month = "5th - 7th September ", organization = "Universidad de Salamanca (Salamanca, Espa{\~n}a)", pages = "224-235", publisher = "Springer", series = "Lecture Notes in Networks and Systems", title = "{G}amifying the classroom for the acquisition of skills associated with {M}achine {L}earning: a two-year case study", url = "doi.org/10.1007/978-3-031-18409-3_22", volume = "532", year = "2022", } @conference{BAmiri2021, author = "B. Amiri and Antonio Manuel G{\'o}mez-Orellana and Pedro Antonio Guti{\'e}rrez and K. Dahmani and R. Diz{\`e}ne", booktitle = "The First International Conference on Renewable Energy Advanced Technologies and Applications (ICREATA'21)", isbn = "978-9931-9819-0-9", keywords = "Algerian Sahara, Artificial Neural Networks, Estimation, Solar irradiation, Tilted plane", month = "25th-27th October", organization = "Research Unit for Renewable Energies in Saharan Region, Adrar", pages = "116-117", title = "{M}ultilayer {P}erceptron and {C}ascade {F}orward {N}eural {N}etwork for {S}hort-term {T}ilted {I}rradiation {E}stimation in {A}lgerian {S}ahara", year = "2021", } @conference{BarberoECOCOrdinalIWANN, author = "Javier Barbero-G{\'o}mez and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Automatic classification tasks have been revolutionized by Convolutional Neural Networks (CNNs), but the focus has been on binary and nominal classification tasks. Only recently, ordinal classification (where class labels present a natural ordering) has been tackled through the framework of CNNs, such as adapting the classic Proportional Odds Model to deep architectures. Also, ordinal classification datasets commonly present a high imbalance in the number of samples of each class, making it an even harder problem. In this work, we present a new CNN architecture based on the Ordinal Binary Decomposition (OBD) technique using Error-Correcting Output Codes (ECOC) and show how it can improve performance over previously proposed methods.", booktitle = "2021 International Work-conference on Artificial Neural Networks (IWANN 2021)", doi = "10.1007/978-3-030-85099-9_1", isbn = "978-3-030-85029-6", issn = "0302-9743", keywords = "Artificial Neural Networks, Ordinal Classification", month = "16nd-18th June", number = "Part II", organization = "Online", pages = "3-13", publisher = "Springer", series = " Lecture Notes in Computer Science (LNCS)", title = "{E}rror-correcting output codes in the framework of deep ordinal classification", url = "doi.org/10.1007/978-3-030-85099-9_1", volume = "12862", year = "2021", } @conference{GuijoCAEPIA2021, author = "David Guijo-Rubio and V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Time Series Ordinal Classification (TSOC) is yet an unexplored field of machine learning consisting in the classification of time series whose labels follow a natural order relationship between them. In this context, a well-known approach for time series nominal classification was previously used: the Shapelet Transform (ST). The exploitation of the ordinal information was included in two steps of the ST algorithm: 1) by using the Pearson's determination coefficient (R²) for computing the quality of the shapelets, which favours shapelets with better ordering, and 2) by applying an ordinal classifier instead of a nominal one to the transformed dataset. For this, the distance between labels was represented by the absolute value of the difference between the corresponding ranks, i.e. by the L1 norm. In this paper, we study the behaviour of different Lp norms for representing class distances in ordinal regression, evaluating 9 different Lp norms with 7 ordinal time series datasets from the UEA-UCR time series classification repository and 10 different ordinal classifiers. The results achieved demonstrate that the Pearson's determination coefficient using the L1.9 norm in the computation of the difference between the shapelet and the time series labels achieves a significantly better performance when compared to the rest of the approaches, in terms of both Correct Classification Rate (CCR) and Average Mean Absolute Error (AMAE). ", booktitle = "Proceedings of the XIX Conference of the Spanish Association for Artificial Intelligence (CAEPIA)", doi = "10.1007/978-3-030-85713-4_5", isbn = "978-3-030-85712-7", issn = "0302-9743", keywords = "Lp norms, TSOC, time series, L2, L1, ordinal classification, ", month = "22nd-24th September", organization = "Malaga, Spain", pages = "44-53", publisher = "Springer", series = " Lecture Notes in Artificial Intelligence (LNAI)", title = "{S}tudying the effect of different {L}p norms in the context of {T}ime {S}eries {O}rdinal {C}lassification", url = "doi.org/10.1007/978-3-030-85713-4_5", volume = "12882", year = "2021", } @conference{ReluVictorCAEPIA2021, author = "V{\'i}ctor Manuel Vargas and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Activation functions are used in neural networks as a tool to introduce non-linear transformations into the model and, thus, enhance its representation capabilities. They also determine the output range of the hidden layers and the final output. Traditionally, artificial neural networks mainly used the sigmoid activation function as the depth of the network was limited. Nevertheless, this function tends to saturate the gradients when the number of hidden layers increases. For that reason, in the last years, most of the works published related to deep learning and convolutional networks use the Rectified Linear Unit (ReLU), given that it provides good convergence properties and speeds up the training process thanks to the simplicity of its derivative. However, this function has some known drawbacks that gave rise to new proposals of alternatives activation functions based on ReLU. In this work, we describe, analyse and compare different recently proposed alternatives to test whether these functions improve the performance of deep learning models regarding the standard ReLU.", booktitle = "Proceedings of the XIX Conference of the Spanish Association for Artificial Intelligence (CAEPIA)", doi = "10.1007/978-3-030-85713-4_4", isbn = "978-3-030-85712-7", issn = "0302-9743", keywords = "analysis activations, RELU, RELU activations, deep learning", month = "22nd-24th September", organization = "Malaga, Spain", pages = "33-43", publisher = "Springer", series = " Lecture Notes in Artificial Intelligence (LNAI)", title = "{R}e{LU}-based activations: analysis and experimental study for deep learning", url = "doi.org/10.1007/978-3-030-85713-4_4", volume = "12882", year = "2021", } @conference{FuzzyORCA2021, author = "Francisco Javier Rodriguez-Lozano and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Jose Manuel Soto-Hidalgo and Juan Carlos G{\'a}mez-Granados", abstract = "Classification and regression techniques are two of the main tasks considered by the Machine Learning area. They mainly depend on the target variable to predict. In this context, ordinal classification represents an intermediate task, which is focused on the prediction of nominal variables where the categories follow a specific intrinsic order given by the problem. Nevertheless, the integration of different algorithms able to solve ordinal classification problems is often unavailable in most of existing Machine Learning software, which hinders the use of new approaches. Therefore, this paper focuses on the incorporation of an ordinal classification algorithm (NSLVOrd) in one of the most complete ordinal regression frameworks, 'Ordinal Regression and Classification Algorithms framework (ORCA)' by using both fuzzy rules and the JFML library. The use of NSLVOrd in the ORCA tool as well as a case study with a real database are shown where the obtained results are promising.", booktitle = "Proceedings of the IEEE International Conference on Fuzzy Systems (Fuzz-IEEE2021)", doi = "10.1109/FUZZ45933.2021.9494526", editor = "IEEE", isbn = "978-1-6654-4407-1", issn = "1558-4739", keywords = "ORCA, Fuzzy ORCA, Fuzzy, JFML Library, NSLVOrd", month = "11th-14th July", organization = " Luxembourg, Luxembourg", publisher = "IEEE Press", title = "{E}nhancing the {ORCA} framework with a new {F}uzzy {R}ule {B}ase {S}ystem implementation compatible with the {JFML} library", url = "doi.org/10.1109/FUZZ45933.2021.9494526", year = "2021", } @conference{CECMartin2020, author = "Alejandro Mart{\'i}n and Ra{\'u}l Lara-Cabrera and V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and David Camacho", abstract = "The adjustment of the hyperparameters and network structure of Convolutional Neural Networks (CNNs) composes an important step towards building effective, but still efficient learning models. The selection of the best configuration is a problem-dependent task that involves to explore an enormous and complex search space. Due to this reason, the use of heuristicbased search fits perfectly within this task, seeking to obtain a near to optimal solution in a complex and large exploratory space. This paper presents SCRODeep, a self-adapting algorithm based on a statistically-driven Coral Reef Optimisation algorithm (SCRO), for the selection of the most adequate CNNs architecture in a particular domain. This metaheuristic has been designed to navigate through a search space where the architecture (defining the particular set of layers, including convolutional or pooling layers), and the hyperparameters of the network (i.e. activation functions, number of units or the kernel initializer, among others) are represented, but where the connections weights and bias are inferred using typical CNNs optimisation algorithms. In contrast to other approaches, where the use of a metaheuristic implies in turn to fix a series of hyperparameters (i.e. the mutation probability in a genetic algorithm), our approach follows a selfparametrisation perspective, thus removing the necessity of fixing these values. The method has been tested in the design of CNNs for image classification, showing that SCRODeep is able to find competitive solutions, while the complexity of the architectures found is constrained.", booktitle = "Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC2020) ", doi = "10.1109/CEC48606.2020.9185914", isbn = "978-1-7281-6926-2", keywords = "Convolutional Neural Networks, Coral Reef based optimisation, architecture definition, optimisation", month = "19th-24th July", organization = "Glasgow, UK", pages = "1--8", title = "{S}tatistically-driven {C}oral {R}eef metaheuristic for automatic hyperparameter setting and architecture design of {C}onvolutional {N}eural {N}etworks", url = "doi.org/10.1109/CEC48606.2020.9185914", year = "2020", } @conference{GuijoAALTD2020, author = "David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Anthony Bagnall and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", booktitle = "Proceedings of the 5th Workshop on Advances Analytics and Learning on Temporal Data", editor = "Springer", isbn = "978-3-030-65741-3", month = "18th September", organization = "Ghent, Belgium", pages = "19--29", series = " Lecture Notes in Artificial Intelligence (LNAI)", title = "{O}rdinal versus nominal time series classification", url = "doi.org/10.1007/978-3-030-65742-0_2", volume = "12588", year = "2020", } @conference{IJCNNGuijo2020, author = "David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Anthony Bagnall and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", booktitle = "Proceedings of the 2020 IEEE International Joint Conference on Neural Networks (IJCNN2020) ", doi = "10.1109/IJCNN48605.2020.9207200", isbn = "978-1-7281-6926-2", month = "19th-24th July", organization = "Glasgow, UK", pages = "1--8", title = "{T}ime series ordinal classification via shapelets", url = "ieeexplore.ieee.org/document/9207200", year = "2020", } @conference{DoradoUCO2019, author = "Manuel Dorado-Moreno and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", booktitle = "VII Congreso Cient{\'i}fico de Investigadores en Formaci{\'o}n", isbn = "978-84-9927-341-9", month = "6th-7th Febreaury", organization = "C{\'o}rdoba, Spain", pages = "629--632", publisher = "UCO Press", series = "Creando Redes Doctorales Vol. VII: Investiga y Comunica", title = "{P}redicci{\'o}n ordinal de rampas de viento usando {E}cho {S}tate {N}etworks de complejidad reducida", volume = "III", year = "2019", } @conference{2769522982019, author = "David Guijo-Rubio and Villal{\'o}n-Vaquero, Pedro J. and Pedro Antonio Guti{\'e}rrez and Ayll{\'o}n, Mar{\'i}a Dolores and Javier Brice{\~n}o and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", booktitle = "Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL2019)", doi = "10.1007/978-3-030-33617-2_11", isbn = "978-3-030-33606-6", month = "14th-16th November", organization = "Manchester, UK", pages = "97-104", series = "Lecture Notes in Computer Science (LNCS)", title = "{M}odelling survival by machine learning methods in liver transplantation: application to the {UNOS} dataset", url = "doi.org/10.1007/978-3-030-33617-2_11", volume = "11872", year = "2019", } @conference{GuijoShapelets2019IDEAL, author = "David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and R. Tavenard and Anthony Bagnall", booktitle = "Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL2019)", doi = "10.1007/978-3-030-33607-3_16", isbn = "978-3-030-33606-6", month = "14th-16th November", organization = "Manchester, UK", pages = "137-144", series = "Lecture Notes in Computer Science (LNCS)", title = "{A} hybrid approach to time series classification with shapelets", url = "doi.org/10.1007/978-3-030-33607-3_16", volume = "11871", year = "2019", } @conference{BillelCongreso1, author = "Billel Amiri and Antonio Manuel G{\'o}mez-Orellana and Pedro Antonio Guti{\'e}rrez and Rabah Diz\'ene and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Dahmani Kahina", abstract = "This work applies evolutionary product unit neural networks (EPUNNs) to estimate global inclined irradiation at real time and predict it 10 minutes in advance. Both tasks are accomplished simultaneously, by using one single model with two outputs. One advantage of our approach is that the predictions of inclined irradiation are obtained without the need of a series of historical data. In this way, the model only considers one measured input variable, which is the horizontal global irradiation at the previous instant. Besides, the evolutionary algorithm used to optimize the network allows us to obtain the best adapted topology of the model with respect to the number of hidden neurons and synaptic connections. Very promising results are obtained, where the inclined irradiation I β (t) is estimated with an accuracy of 5.10% of nRMSE, while it is predicted 10 minutes in advance with an accuracy of 16.97%.", booktitle = "Proceedings of the 2019 International Conference of Computer Science and Renewable Energies (ICCSRE)", doi = "10.1109/ICCSRE.2019.8807613", isbn = "978-1-7281-0827-8", keywords = "solar irradiation, tilted plane, forecasting, artificial neural network, evolutionary learning, product units, evolutionary programming", month = "22th-24th July", organization = "Agadir, Morocco", publisher = "IEEE Press", title = "{T}en {M}inutes {S}olar {I}rradiation {F}orecasting on {I}nclined {P}lane using {E}volutionary {P}roduct {U}nit {N}eural {N}etworks", url = "doi.org/10.1109/ICCSRE.2019.8807613", year = "2019", } @conference{Vargas2019Deep, author = "V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper proposes a deep neural network model for ordinal regression problems based on the use of a probabilistic ordinal link function in the output layer. This link function reproduces the Proportional Odds Model (POM), a statistical linear model which projects each pattern into a 1-dimensional space. In our case, the projection is estimated by a non-linear deep neural network. After that, patterns are classified using a set of ordered thresholds. In order to further improve the results, we combine this link function with a loss cost that takes the distance between classes into account, based on the weighted Kappa index. The experiments are based on two ordinal classification problems, and the statistical tests confirm that our ordinal network outperforms the nominal version and other proposals considered in the literature.", booktitle = "Proceedings of the International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC 2019)", doi = "10.1007/978-3-030-19651-6_43", isbn = "978-3-030-19650-9", keywords = "Ordinal regression, Ordinal classification, Proportional odds model, Deep learning, Convolutional neural network ", month = "3-7 junio", organization = "Almer{\'i}a (Espa{\~n}a)", pages = "441-451", series = " Lecture Notes in Computer Science (LNCS)", title = "{D}eep {O}rdinal {C}lassification {B}ased on the {P}roportional {O}dds {M}odel", url = "doi.org/10.1007/978-3-030-19651-6_43", volume = "11487", year = "2019", } @conference{Perez2019Exploiting, author = "Mar{\'i}a P{\'e}rez-Ortiz and Peter Tino and Rafal Mantiuk and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Data augmentation is rapidly gaining attention in machinelearning. Synthetic data can be generated by simple transfor-mations or through the data distribution. In the latter case,the main challenge is to estimate the label associated to newsynthetic patterns. This paper studies the effect of generat-ing synthetic data by convex combination of patterns and theuse of these as unsupervised information in a semi-supervisedlearning framework with support vector machines, avoidingthus the need to label synthetic examples. We perform ex-periments on a total of 53 binary classification datasets. Ourresults show that this type of data over-sampling supportsthe well-known cluster assumption in semi-supervised learn-ing, showing outstanding results for small high-dimensionaldatasets and imbalanced learning problems.", booktitle = "Proceedings of the Thirty-Third AAAI (Association for the Advancement of Artificial Intelligence) Conference on Artificial Intelligence (AAAI'19)", doi = "10.1609/aaai.v33i01.33014715", isbn = "978-1-57735-809-1", issn = "2159-5399", month = "27th February", organization = "Honolulu,Hawaii, USA", pages = "4715-4722", title = "{E}xploiting synthetically generated data with semi-supervised learning for small and imbalanced datasets", url = "doi.org/10.1609/aaai.v33i01.33014715", year = "2019", } @conference{Aliaksei Mikhailiuk2018, author = "Aliaksei Mikhailiuk and Mar{\'i}a P{\'e}rez-Ortiz and Rafal K. Mantiuk", abstract = "TID2013 is a subjective image quality assessment dataset with a wide range of distortion types and over 3000 images. The dataset has proven to be a challenging test for objective quality metrics. The dataset mean opinion scores were obtained by collecting pairwise comparison judgments using the Swiss tournament system, and averaging votes of observers. However, this approach differs from the usual analysis of multiple pairwise comparisons, which involves psychometric scaling of the comparison data using either Thurstone or Bradley-Terry models. In this paper we investigate how quality scores change when they are computed using such psychometric scaling instead of averaging vote counts. In order to properly scale TID2013 quality scores, we conduct four additional experiments of two different types, which we found necessary to produce a common quality scale: comparisons with reference images, and cross-content comparisons. We demonstrate on a fifth validation experiment that the two additional types of comparisons are necessary and in conjunction with psychometric scaling improve the consistency of quality scores, especially across images depicting different contents.", booktitle = "International Conference on Quality of Multimedia Experience", doi = "10.1109/QoMEX.2018.8463376", issn = "2472-7814", title = "{P}sychometric scaling of {TID}2013 dataset", url = "doi.org/10.1109/QoMEX.2018.8463376", year = "2018", } @conference{182018, author = "Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "In this work, a methodology for the detection and prediction of Segments containing very high Significant Wave Height (SSWH) values in oceans, is proposed. This procedure is needed to determine potential changes in a long-term future operational environment of marine and coastal structures. The methodology transforms the time series into a sequence of labeled segments, using a genetic algorithm (GA) in combination with a likelihood-based local search. Then, Artificial Neural Networks models (ANN) are trained with a Multiobjective Evolutionary Algorithm (MOEA), with the objective of predicting the occurrence or not of SSWH events. The MOEA is designed to optimize the global accuracy and individual sensitivities for both classes, due to the imbalanced nature of the problem (SSWH are rarer than non SSWH). The methodology is applied to a time series collected from a buoy of the Gulf of Alaska, showing that the GA is able to group SSWH events in a specific cluster and that the MOEA obtains accurate ANN models when predicting these events.", booktitle = "VI Congreso Cientı́fico de Investigadores en Formaci{\'o}n", editor = "UCOPress. Editorial Universidad de C{\'o}rdoba", isbn = "978-84-9927-239-9", month = "24th-26th January", organization = "C{\'o}rdoba, Spain", pages = "509-512", publisher = "Universidad de C{\'o}rdoba", series = "Creando Redes Doctorales: La generaci{\'o}n del conocimiento", title = "{D}etecci{\'o}n y predicci{\'o}n de segmentos de altura de olas extremas", url = "www.uco.es/ucopress/index.php/en/?option=com_hikashop{\&}ctrl=product{\&}task=show{\&}cid=620{\&}name=ebook-creando-redes-doctorales-vol-vi-la-generacion-del-conocimiento{\&}Itemid=976{\&}category_pathway=", volume = "6", year = "2018", } @conference{Guijo2018Time, author = "David Guijo-Rubio and Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and Alicia Troncoso and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Time series clustering is the process of grouping time series with respect to their similarity or characteristics. Previous approaches usually combine a specific distance measure for time series and a standard clustering method. However, these approaches do not take the similarity of the different subsequences of each time series into account, which can be used to better compare the time series objects of the dataset. In this paper, we propose a novel technique of time series clustering based on two clustering stages. In a first step, a least squares polynomial segmentation procedure is applied to each time series, which is based on a growing window technique that returns different-length segments. Then, all the segments are projected into same dimensional space, based on the coefficients of the model that approximates the segment and a set of statistical features. After mapping, a first hierarchical clustering phase is applied to all mapped segments, returning groups of segments for each time series. These clusters are used to represent all time series in the same dimensional space, after defining another specific mapping process. In a second and final clustering stage, all the time series objects are grouped. We consider internal clustering quality to automatically adjust the main parameter of the algorithm, which is an error threshold for the segmentation. The results obtained on 84 datasets from the UCR Time Series Classification Archive have been compared against two state-of-the-art methods, showing that the performance of this methodology is very promising.", booktitle = "Proceedings of Third Bilbao Data Science Workshop (BiDAS 3)", month = "8th-9th November", organization = "Bilbao (Spain)", title = "{T}ime series clustering based on the characterisation of segment typologies", year = "2018", } @conference{EPSC2018, author = "Athanasia Nikolaou and Pedro Antonio Guti{\'e}rrez and Antonio Manuel Dur{\'a}n-Rosal and Francisco Fernandez-Navarro and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Mar{\'i}a P{\'e}rez-Ortiz", booktitle = "Proceedings of the 2018 European Planetary Science Congress", month = "16th-21st September", organization = "Berlin (Germany)", series = "EPSC2018-829-1", title = "{D}etection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm", url = "meetingorganizer.copernicus.org/EPSC2018/EPSC2018-829-1.pdf", volume = "12", year = "2018", } @conference{222018, author = "David Guijo-Rubio and Antonio Manuel Dur{\'a}n-Rosal and Antonio G{\'o}mez-Orellana and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Wave height prediction is an important task for ocean and marine resource management. Traditionally, regression techniques are used for this prediction, but estimating continuous changes in the corresponding time series can be very difficult. With the purpose of simplifying the prediction, wave height can be discretised in consecutive intervals, resulting in a set of ordinal categories. Despite this discretisation could be performed using the criterion of an expert, the prediction could be biased to the opinion of the expert, and the obtained categories could be unrepresentative of the data recorded. In this paper, we propose a novel automated method to categorise the wave height based on selecting the most appropriate distribution from a set of well-suited candidates. Moreover, given that the categories resulting from the discretisation show a clear natural order, we propose to use different ordinal classifiers instead of nominal ones. The methodology is tested in real wave height data collected from two buoys located in the Gulf of Alaska and South Kodiak. We also incorporate reanalysis data in order to increase the accuracy of the predictors. The results confirm that this kind of discretisation is suitable for the time series considered and that the ordinal classifiers achieve outstanding results in comparison with nominal techniques.", booktitle = "Proceedings of the 2018 International Conference on Intelligent Data Engineering and Automated Learning (IDEAL2018)", doi = "10.1007/978-3-030-03496-2_20", isbn = "978-3-030-03496-2", keywords = "Wave height prediction, Distribution fitting, Time series discretisation, Autoregressive models, Ordinal classification ", month = "21st-23rd November", organization = "Madrid, Spain", pages = "171-179", series = "Lecture Notes in Computer Science (LNCS)", title = "{D}istribution-{B}ased {D}iscretisation and {O}rdinal {C}lassification {A}pplied to {W}ave {H}eight {P}rediction", url = "doi.org/10.1007/978-3-030-03496-2_20", volume = "11315", year = "2018", } @conference{Dorado2018CAEPIA, author = "Manuel Dorado-Moreno and Pedro Antonio Guti{\'e}rrez and Sancho Salcedo-Sanz and Luis Prieto and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Las Renovables son la fuente de energı́a que m{\'a}s ha crecido en los {\'u}ltimos a{\~n}os a nivel mundial. En particular, la energı́a e{\'o}lica en Europa es actualmente la que tiene un mayor crecimiento, estando su capacidad de producci{\'o}n en la segunda posici{\'o}n, por detr{\'a}s del gas natural. Existen una serie de problemas que complican la integraci{\'o}n del recurso e{\'o}lico en la red el{\'e}ctrica. Uno de ellos es conocido como rampas de viento, que son incrementos o decrementos de gran magnitud en la velocidad del viento en un tiempo reducido. Estas rampas de viento pueden da{\~n}ar las turbinas en los parques e{\'o}licos, ası́ como reducir los ingresos generados a partir de la producci{\'o}n del parque. Actualmente, la mejor forma de afrontar este problema es predecir estas rampas de viento, de forma que se puedan parar las turbinas con suficiente antelaci{\'o}n, evitando ası́ los da{\~n}os que puedan producirse. Para realizar esta predicci{\'o}n, se suelen utilizar modelos que puedan aprovechar la informaci{\'o}n temporal. Uno de los modelos m{\'a}s conocidos con estas caracterı́sticas son las redes neuronales recurrentes. En este trabajo utilizaremos las conocidas como Echo State Networks (ESNs), las cuales han demostrado obtener un buen rendimiento en predicci{\'o}n de series temporales. En concreto, proponemos utilizar ESNs de complejidad reducida para afrontar un problema de predicci{\'o}n de rampas de viento en tres parques e{\'o}licos en Espa{\~n}a. A nivel metodol{\'o}gico, se comparan tres arquitecturas diferentes de red, dependiendo de la configuraci{\'o}n de las conexiones de la capa de entrada con el reservoir o directamente con la capa de salida. Los resultados muestran que, por lo general, los mejores resultados son obtenidos por la estructura Delay Line Reservoir with Feedback (DLRB) y que el aumento en el rendimiento obtenido por la arquitectura de Doble reservoir con respecto a la arquitectura de Simple reservoir es mı́nima, y teniendo en cuenta el gran aumento de complejidad computacional de la arquitectura Doble, concluimos que los mejores resultados son obtenidos por la combinaci{\'o}n de la estructura DLRB con la arquitectura Simple.", booktitle = "Proceedings of the 2018 Conference of the Spanish Association for Artificial Intelligence (CAEPIA2018)", isbn = "978-84-09-05643-9", keywords = "Echo state networks, energ{\'i}a e{\'o}lica, Clasificaci{\'o}n ordinal, Rampas de viento, Redes neuronales recurrentes", month = "23rd-26th October", organization = "Granada (Spain)", pages = "132-138", title = "{P}redicci{\'o}n ordinal de rampas de viento usando {E}cho {S}tate {N}etworks de complejidad reducida", url = "sci2s.ugr.es/caepia18/proceedings/docs/CAEPIA2018_paper_88.pdf", year = "2018", } @conference{DiazCAEPIA2018, author = "Miguel Diaz-Lozano and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Carlos Casanova-Mateo and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Los eventos de muy baja visibilidad producidos por niebla son un problema recurrente en ciertas zonas cercanas a rı́os y grandes monta{\~n}as, que afectan fuertemente a la actividad humana en diferentes aspectos. Este tipo de eventos pueden llegar a suponer costes materiales e incluso humanos muy importantes. Uno de los sectores m{\'a}s influenciados por las condiciones de muy baja visibilidad son los medios de transporte, fundamen- talmente el transporte a{\'e}reo, cuya actividad se ve seriamente mermada, provocando retrasos, cancelaciones y, en el peor de los casos, terribles accidentes. En el aeropuerto de Valladolid son muy frecuentes las situaciones de baja visibilidad por niebla, especialmente en los meses considerados de invierno (noviembre, diciembre, enero y febrero). Esto afecta de forma directa a la manera en la que operan los vuelos de este aeropuerto. De esta forma, es muy importante conocer las posibles condiciones de niebla a corto plazo para aplicar procedimientos de seguridad y organizaci{\'o}n dentro del aeropuerto. En el presente artı́culo se propone el uso de diferentes modelos de ventanas din{\'a}micas y est{\'a}ticas junto con clasificadores de aprendizaje autom{\'a}tico, para la predicci{\'o}n de niveles de niebla. En lugar de abordar el problema como una tarea de regresi{\'o}n, la variable de inter{\'e}s para la caracterizaci{\'o}n del nivel de visibilidad en el aeropuerto (Rango Visual de Pista, RVR) se discretiza en 3 categorı́as, lo que aporta mayor robustez a los modelos de clasificaci{\'o}n obtenidos. Los resultados indican que una combinaci{\'o}n de ventana din{\'a}mica con ventana est{\'a}tica, junto con modelos de clasificaci{\'o}n basados en Gradient Boosted Trees es la metodologı́a que proporciona los mejores resultados.", booktitle = "Proceedings of the 2018 Conference of the Spanish Association for Artificial Intelligence (CAEPIA2018)", isbn = "978-84-09-05643-9", keywords = "Series temporales, Eventos de baja visibilidad, modelos autorregresivos, predicci{\'o}n", month = "23rd-26th October", organization = "Granada (Spain)", pages = "833-838", title = "{A}lgoritmos de aprendizaje autom{\'a}tico para predicci{\'o}n de niveles de niebla usando ventanas est{\'a}ticas y din{\'a}micas", url = "sci2s.ugr.es/caepia18/proceedings/docs/CAEPIA2018_paper_122.pdf", year = "2018", } @conference{CamachoCAEPIA2018, author = "Julio Camacho-Ca{\~n}am{\'o}n and Marı́a-Victoria Guiote and Antonio-Marı́a Santos-Bueno and Ester Rodrı́guez-C{\'a}ceres and Elvira Carmona-Asenjo and Juan-Antonio Vallejo-Casas and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "La enfermedad de Parkinson se caracteriza por un descenso de la densidad de transportadores presin{\'a}pticos de dopamina en los n{\'u}cleos de la base. El m{\'e}todo habitual de clasificaci{\'o}n est{\'a} basado en la observaci{\'o}n y el an{\'a}lisis cualitativo de las im{\'a}genes obtenidas tras la administraci{\'o}n de 123 I-ioflupano al paciente que se va a diagnosticar. De esta forma, las t{\'e}cnicas recientes de neuroimagen, como la imagen dopa- min{\'e}rgica utilizando tomografı́a computarizada por emisi{\'o}n de fot{\'o}n {\'u}nico (SPECT-CT) con 123 I-ioflupano (DaTSCAN), han demostrado detectar la enfermedad, incluso en etapas tempranas. Sin embargo, los comit{\'e}s internacionales recomiendan un an{\'a}lisis cuantitativo, asociado a la construcci{\'o}n de modelos de apoyo que complementen el diagn{\'o}stico visual. El objetivo del presente estudio es establecer un sistema de apoyo a la decisi{\'o}n, mediante la clasificaci{\'o}n ordinal de las im{\'a}genes asociadas a los diferentes grados de afectaci{\'o}n de la enfermedad mediante t{\'e}cnicas de aprendizaje autom{\'a}tico. La base de datos utilizada est{\'a} formada por 316 estudios realizados a pacientes entre septiembre de 2015 y mayo de 2018, distribuidos en tres grupos: 191 no padecen la enfermedad de Parkinson, 55 la padecen con un nivel de afectaci{\'o}n leve y 70 con un nivel de afectaci{\'o}n grave. Tras la administraci{\'o}n intravenosa de 5 mCi (185 MBq), se realiz{\'o} una SPECT-CT, preprocesando y normalizando espacialmente las im{\'a}genes. Como clasificador ordinal utilizamos un m{\'e}todo de regresi{\'o}n logı́stica, que nos permite obtener las caracterı́sticas (v{\'o}xeles de la imagen) m{\'a}s informativas para la tarea de clasifica- ci{\'o}n. El mejor modelo alcanz{\'o} un error absoluto medio m{\'a}ximo (MMAE) de 0,4857, tras la aplicaci{\'o}n de un dise{\~n}o experimental de tipo 5-fold. El an{\'a}lisis de los v{\'o}xeles m{\'a}s informativos, de acuerdo con el modelo obtenido, destaca regiones del cerebro que no son consideradas habitualmente por los especialistas para el diagn{\'o}stico visual.", booktitle = "Proceedings of the 2018 Conference of the Spanish Association for Artificial Intelligence (CAEPIA2018)", isbn = "978-84-09-05643-9", keywords = "Enfermedad de Parkinson, SPECTCT, aprendizaje autom{\'a}tico, clasificaci{\'o}n ordinal", month = "23rd-26th October", organization = "Granada (Spain)", pages = "167-172", title = "{C}lasificaci{\'o}n ordinal de los grados de afectaci{\'o}n de la enfermedad de {P}arkinson empleando im{\'a}genes de transportadores presin{\'a}pticos de dopamina", url = "sci2s.ugr.es/caepia18/proceedings/docs/CAEPIA2018_paper_111.pdf", year = "2018", } @conference{CROCAEPIA2018, author = "Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The exponential increase of available temporal data encourages the development of new automatic techniques to reduce the number of points of time series. In this paper, we propose a novel modification of the coral reefs optimization algorithm (CRO) to reduce the size of the time series with the minimum error of approximation. During the evolution, the solutions are locally optimised and reintroduced in the optimization process. The hybridization is performed using two well-known state-of-the-art algorithms, namely Bottom-Up and Top-Down. The resulting algorithm, called memetic CRO (MCRO), is compared against standard CRO, its statistically driven version (SCRO) and their hybrid versions (HCRO and HSCRO, respectively). The methodology is tested in 15 time series collected from different sources, including financial problems, oceanography data, and cardiology signals, among others, showing that the best results are obtained by MCRO.", booktitle = "Proceedings of the 2018 Conference of the Spanish Association for Artificial Intelligence (CAEPIA2018)", doi = "10.1007/978-3-030-00374-6_20", isbn = "978-3-030-00373-9", issn = "0302-9743", keywords = "Time series size reduction, Segmentation, Coral reefs optimization, Memetic algorithms ", month = "23rd-28th September", organization = "Granada (Spain)", pages = "209-218", series = "Lecture Notes in Computer Science", title = "{A}n {E}mpirical {V}alidation of a {N}ew {M}emetic {CRO} {A}lgorithm for the {A}pproximation of {T}ime {S}eries", url = "doi.org/10.1007/978-3-030-00374-6_20", volume = "11160", year = "2018", } @conference{152018, author = "Manuel Dorado-Moreno and Pedro Antonio Guti{\'e}rrez and Sancho Salcedo-Sanz and Luis Prieto and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Renewable energy is the fastest growing source of energy in the last years. In Europe, wind energy is currently the energy source with the highest growing rate and the second largest production capacity, after gas energy. There are some problems that difficult the integration of wind energy into the electric network. These include wind power ramp events, which are sudden differences (increases or decreases) of wind speed in short periods of times. These wind ramps can damage the turbines in the wind farm, increasing the maintenance costs. Currently, the best way to deal with this problem is to predict wind ramps beforehand, in such way that the turbines can be stopped before their occurrence, avoiding any possible damages. In order to perform this prediction, models that take advantage of the temporal information are often used. One of the most well-known models in this sense are recurrent neural networks. In this work, we consider a type of recurrent neural networks which is known as Echo State Networks (ESNs) and has demonstrated good performance when predicting time series. Specifically, we propose to use the Minimum Complexity ESNs in order to approach a wind ramp prediction problem at three wind farms located in the Spanish geography. We compare three different network architectures, depending on how we arrange the connections of the input layer, the reservoir and the output layer. From the results, a single reservoir for wind speed with delay line reservoir and feedback connections is shown to provide the best performance.", booktitle = "Proceedings of the 2018 International Conference on Intelligent Data Engineering and Automated Learning (IDEAL2018)", doi = "10.1007/978-3-030-03496-2_21", isbn = "978-3-030-03496-2", keywords = "Echo state networks, Wind energy, Ordinal classification, Wind power ramp events, Recurrent neural networks ", month = "21st-23rd November", organization = "Madrid, Spain", pages = "180-187", series = "Lecture Notes in Computer Science (LNCS)", title = "{W}ind power ramp events ordinal prediction using minimum complexity echo state networks", url = "doi.org/10.1007/978-3-030-03496-2_21", volume = "11315", year = "2018", } @conference{Duran2018bioma, author = "Antonio Manuel Dur{\'a}n-Rosal and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The amount of data available in time series is recently increasing in an exponential way, making difficult time series preprocessing and analysis. This paper adapts different methods for time series representation, which are based on time series segmentation. Specifically, we consider a particle swarm optimization algorithm (PSO) and its barebones exploitation version (BBePSO). Moreover, a new variant of the BBePSO algorithm is proposed, which takes into account the ositions of the particles throughout the generations, where those close in time are given more importance. This methodology is referred to as weighted BBePSO (WBBePSO). The solutions obtained by all the algorithms are finally hybridised with a local search algorithm, combining simple segmentation strategies (Top-Down and Bottom-Up). WBBePSO is tested in 13 time series and compared against the rest of algorithms, showing that it leads to the best results and obtains consistent representations.", booktitle = "Bioinspired Optimization Methods and their Applications (BIOMA2018)", doi = "10.1007/978-3-319-91641-5_11", isbn = "978-3-319-91640-8", month = "16th-18th May", organization = "Paris (France)", pages = "126--137", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science (LNCS)", title = "{H}ybrid {W}eighted {B}arebones {E}xploiting {P}article {S}warm {O}ptimization {A}lgorithm for {T}ime {S}eries {R}epresentation", url = "dx.doi.org/10.1007/978-3-319-91641-5_11", volume = "10835", year = "2018", } @conference{IJCNN_2018, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and Peter Tino and Carlos Casanova-Mateo and Sancho Salcedo-Sanz", abstract = "Weather and atmospheric patterns are often persistent. The simplest weather forecasting method is the so-called persistence model, which assumes that the future state of a system will be similar (or equal) to the present state. Machine learning (ML) models are widely used in different weather forecasting applications, however, these models need to be compared to the persistence model to analyse whether ML provides a competitive solution to the problem at hand. In this paper we devise a new model for predicting low-visibility in airports using the concepts of mixture of experts. Visibility level is coded as two different ordered categorical variables (cloud height and runway visual height). The underlying system in this application is stagnant approximately in 90% of the cases and standard ML models fail to improve on the performance of the persistence model. Because of this, instead of trying to simply beat the persistence model using ML we use this persistence as a baseline and learn a ordinal neural network model that refines its results by focusing on learning weather fluctuations. The results show that the proposal outperforms persistence and other ordinal autoregressive models, especially for longer time horizon predictions and for the runway visual height variable.", booktitle = "Proceedings of the 2018 IEEE International Joint Conference on Neural Networks (IJCNN 2018)", doi = "10.1109/IJCNN.2018.8489179", isbn = "978-1-5090-6014-6", keywords = "mixture of experts, persistence model, dynamic systems, ordinal classification, ordinal regression, autoregressive models, neural networks, low-visibility", month = "8th-13th July", organization = "Rio (Brazil)", pages = "5714--5721", publisher = "IEEE Press", title = "{A} mixture of experts model for predicting persistent weather patterns", url = "doi.org/10.1109/IJCNN.2018.8489179", year = "2018", } @conference{R. Cruz20171, author = "R. Cruz and K. Fernandes and J.F. Pinto Costa and Mar{\'i}a P{\'e}rez-Ortiz and J. S. Cardoso", abstract = "Classification datasets, which feature a skewed class distribution, are said to be class imbalance. Traditional methods favor the larger classes. We propose pairwise ranking as a method for imbalance classification so that learning compares pairs of observations from each class, and therefore both contribute equally to the decision boundary. In previous work, we suggested treating the binary classification as a ranking problem, followed by a threshold mapping to convert back the ranking score to the original classes. In this work, the method is extended to multi-class ordinal classification, and a new mapping threshold is proposed. Results are compared with traditional and ordinal SVMs, and ranking obtains competitive results.", booktitle = "Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2017)", doi = " 10.1007/978-3-319-58838-4_1", isbn = "978-3-319-58837-7", pages = "3-12", series = "Lecture Notes in Computer Science", title = "{O}rdinal {C}lass {I}mbalance with {R}anking", url = "http://dx.doi.org/10.1007/978-3-319-58838-4_1", volume = "10255", year = "2017", } @conference{R. Cruz2017, author = "R. Cruz and K. Fernandes and J.F. Pinto Costa and Mar{\'i}a P{\'e}rez-Ortiz and J.S. Cardoso", abstract = "In classification problems, a dataset is said to be imbalanced when the distribution of the target variable is very unequal. Classes contribute unequally to the decision boundary, and special metrics are used to evaluate these datasets. In previous work, we presented pairwise ranking as a method for binary imbalanced classification, and extended to the ordinal case using weights. In this work, we extend ordinal classification using traditional balancing methods. A comparison is made against traditional and ordinal SVMs, in which the ranking adaption proposed is found to be competitive.", booktitle = "International Work Conference on Artificial Neural Networks (IWANN2017)", doi = "10.1007/978-3-319-59147-6_46", isbn = "978-3-319-59146-9", keywords = "Ordinal classification, Class imbalance, Ranking, SVM ", pages = "538-548", series = "Lecture Notes in Computer Science", title = "{C}ombining {R}anking with {T}raditional {M}ethods for {O}rdinal {C}lass {I}mbalance", url = "http://dx.doi.org/10.1007/978-3-319-59147-6_46", volume = "10306", year = "2017", } @conference{Perez2017, author = "Mar{\'i}a P{\'e}rez-Ortiz and Kelwin Fernandes and Ricardo Cruz and Jaime S. Cardoso and Javier Brice{\~n}o and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Nowadays imbalanced learning represents one of the most vividly discussed challenges in machine learning. In these scenarios, one or some of the classes in the problem have a significantly lower a priori probability, usually leading to trivial or non-desirable classifiers. Because of this, imbalanced learning has been researched to a great extent by means of different approaches. Recently, the focus has switched from binary classification to other paradigms where imbalanced data also arise, such as ordinal classification. This paper tests the application of learning pairwise ranking with multiple granularity levels in an ordinal and imbalanced classification problem where the aim is to construct an accurate model for donor-recipient allocation in liver transplantation. Our experiments show that approaching the problem as ranking solves the imbalance issue and leads to a competitive performance.", booktitle = "IWANN 2017", doi = "10.1007/978-3-319-59147-6_45", isbn = "978-3-319-59146-9", keywords = "Imbalanced data, Ranking, Ordinal classification, Over-sampling ", pages = "525-537", series = "Lecture Notes in Computer Science", title = "{F}ine-to-{C}oarse {R}anking in {O}rdinal and {I}mbalanced {D}omains: {A}n {A}pplication to {L}iver {T}ransplantation", url = "http://dx.doi.org/10.1007/978-3-319-59147-6_45", volume = "10306", year = "2017", } @conference{SanchezIMIBIC2017, author = "Javier S{\'a}nchez-Monedero and Mar{\'i}a P{\'e}rez-Ortiz and A. S{\'a}ez and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", booktitle = "8th IMIBIC Young Investigators Meeting", month = "30-31th May", organization = "C{\'o}rdoba, Spain", pages = "131", title = "{A}dvanced feature extraction and machine learning models to melanoma and {B}reslow index detection", year = "2017", } @conference{CamachoIMIBIC2017, author = "Julio Camacho-Ca{\~n}am{\'o}n and M.V. Guiote-Moreno and A.M. Santos-Bueno and E. Rodr{\'i}guez-C{\'a}ceres and E. Carmona-Asenjo and J.A. Vallejo-Casas and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", booktitle = "8th IMIBIC Young Investigators Meeting", month = "30-31th May", organization = "C{\'o}rdoba, Spain", pages = "88", title = "{I}mage classification of synaptic dopamine transporters 123{I}-{I}oflupane by machine learning techniques", year = "2017", } @conference{232017, author = "Julio Camacho-Ca{\~n}am{\'o}n and Mar{\'i}a Victoria Guiote and Antonio Mª Santos Bueno and Ester Rodr{\'i}guez-C{\'a}ceres and Elvira Carmona Asenjo and Juan Antonio Vallejo Casas and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", booktitle = "Proceedings of the 2017 Annual Congress of the European Association of Nuclear Medicine (EANM17)", doi = "10.1007/s00259-017-3822-1", issn = " 1619-7070", keywords = "Parkinsons disease, SPECT, machine learning, classification, medical image", month = "21st-25th October", number = "2", organization = "Vienna, Austria", pages = "S285-S286", series = "European Journal of Nuclear Medicine and Molecular Imaging", title = "{I}mage classification of synaptic dopamine transporters 123{I}-{I}oflupane by machine learning techniques", url = "doi.org/10.1007/s00259-017-3822-1", volume = "44", year = "2017", } @conference{Camacho-CanamonSEMNIM2017, author = "Julio Camacho-Ca{\~n}am{\'o}n and Victoria Guiote Moreno and Antonio Santos Bueno and Ester Rodr{\'i}guez-C{\'a}ceres and Elvira Carmona Asenjo and Juan Antonio Vallejo Casas and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Aplicar t{\'e}cnicas de aprendizaje autom{\'a}tico para clasificar im{\'a}genes cerebrales funcionales en el diagn{\'o}stico diferencial de trastornos del movimiento.", booktitle = "36.º Congreso de la Sociedad Espa{\~n}ola de Medicina Nuclear e Imagen Molecular", keywords = "Parkinson disease,SPECT,machine learning,classification,medical image", month = "24th-26th October", number = "36", organization = "Palma de Mallorca (Spain)", pages = "5", series = "Revista Espa{\~n}ola de Medicina Nuclear e Imagen Molecular", title = "{C}lasificaci{\'o}n de im{\'a}genes de transportadores sin{\'a}pticos de dopamina con 123{I}-{I}oflupano mediante t{\'e}cnicas de aprendizaje autom{\'a}tico", url = "www.elsevier.es/es-revista-revista-espanola-medicina-nuclear-e-125-congresos-36-congreso-sociedad-espanola-medicina-50-sesion-neurociencias-3399-comunicacion-clasificacin-de-imgenes-de-transportadores-38614", volume = "Sup 1", year = "2017", } @conference{Camacho-CanamonMIUA2017, author = "Julio Camacho-Ca{\~n}am{\'o}n and Mar{\'i}a J. Carreira and Pedro Antonio Guti{\'e}rrez and Ram{\'o}n Iglesias-Rey", abstract = "Cerebral stroke is a cerebrovascular disease caused by an alteration of blood flow to the brain. Rodents are used to experiment with drugs provoking a stroke and studying the effects of different drugs as a measure of the relation of lesion volume to brain volume. Nowadays, clinicians are performing these experiments manually, leading to interhuman errors and not repeatability, of results, as well as being time-consuming tasks. This paper presents a methodology to automate this task, performing an automatic computation of the brain volume from the brain area for each slice of the rodent brain. Although in its initial state, results are very promising, and so work will follow in this way with the computation of lesion volume.", booktitle = "Annual Conference on Medical Image Understanding and Analysis ( MIUA 2017)", doi = "10.1007/978-3-319-60964-5_60", isbn = "978-3-319-60963-8", keywords = "Brain volume, contour model, image segmentation, medical image", month = "11th-13th July", organization = "Edinburgh, United Kingdom", pages = "686-697", publisher = "Springer International Publishing", series = "Communications in Computer and Information Science (CCIS)", title = "{E}stimating rodent brain volume by a deformable contour model", url = "doi.org/10.1007/978-3-319-60964-5_60", volume = "723", year = "2017", } @conference{Gutierrez2017IWANN, author = "Pedro Antonio Guti{\'e}rrez and Mar{\'i}a P{\'e}rez-Ortiz and Alberto Su{\'a}rez", abstract = "The term ordinal regression refers to classification tasks in which the categories have a natural ordering. The main premise of this learning paradigm is that the ordering can be exploited to generate more accurate predictors. The goal of this work is to design class switching ensembles that take into account such ordering so that they are more accurate in ordinal regression problems. In standard (nominal) class switching ensembles, diversity among the members of the ensemble is induced by injecting noise in the class labels of the training instances. Assuming that the classes are interchangeable, the the labels are modified at random. In ordinal class switching, the ordering between classes is taken into account by reducing the transition probabilities to classes that are further apart. In this manner smaller label perturbations in the ordinal scale are favoured. Two different specifications of these transition probabilities are considered; namely, an arithmetic and a geometric decrease with the absolute difference of the class ranks. These types of ordinal class switching ensembles are compared with an ensemble method that does not consider class-switching, a nominal class-switching ensemble, an ordinal variant of boosting, and two state-of-the-art ordinal classifiers based on support vector machines and Gaussian processes, respectively. These methods are evaluated and compared in a total of $15$ datasets, using three different performance metrics. From the results of this evaluation one concludes that ordinal class-switching ensembles are more accurate than standard class-switching ones and than the ordinal ensemble method considered. Furthermore, their performance is comparable to the state-of-the-art ordinal regression methods considered in the analysis. Thus, class switching ensembles with specifically designed transition probabilities, which take into account the relationships between classes, are shown to provide very accurate predictions in ordinal regression problems.", booktitle = "14th International Work-Conference on Artificial and Natural Neural Networks (IWANN2017)", doi = "10.1007/978-3-319-59153-7_36", isbn = "978-3-319-59152-0", keywords = "Class switching, ordinal regression, ensemble learning", month = "14th-16th June", organization = "C{\'a}diz, Spain", pages = "408-419", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science (LNCS)", title = "{C}lass switching ensembles for ordinal regression", url = "dx.doi.org/10.1007/978-3-319-59153-7_36", volume = "10305", year = "2017", } @conference{MaestreGarcia2017, author = "Maestre-Garc{\'i}a, Francisco Javier and Garc{\'i}a-Mart{\'i}nez, Carlos and Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez", abstract = "Real-world classification datasets often present a skewed distribution of patterns, where one or more classes are under-represented with respect to the rest. One of the most successful approaches for alleviating this problem is the generation of synthetic minority samples by convex combination of available ones. Within this framework, adaptive synthetic (ADASYN) sampling is a relatively new method which imposes weights on minority examples according to their learning complexity, in such a way that difficult examples are more prone to be over-sampled. This paper proposes an improvement of the ADASYN method, where the learning complexity of these patterns is also used to decide which sample of the neighbourhood is selected. Moreover, to avoid suboptimal results when performing the random convex combination, this paper explores the application of an iterative greedy algorithm which refines the synthetic patterns by repeatedly replacing a part of them. For the experiments, six binary datasets and four over-sampling methods are considered. The results show that the new version of ADASYN leads to more robust results and that the application of the iterative greedy metaheuristic significantly improves the quality of the generated patterns, presenting a positive effect on the final classification model.", booktitle = "14th International Work-Conference on Artificial and Natural Neural Networks (IWANN2017)", doi = "10.1007/978-3-319-59147-6_44", isbn = "978-3-319-59146-9", keywords = "Over-sampling, imbalanced classification, ADASYN, iterative greedy algorithm, metaheuristics", month = "14th-16th June", organization = "C{\'a}diz, Spain", pages = "513-524", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science (LNCS)", title = "{A}n iterated greedy algorithm for improving the generation of synthetic patterns in imbalanced learning", url = "dx.doi.org/10.1007/978-3-319-59147-6_44", volume = "10305", year = "2017", } @conference{Duran2017, author = "Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and Francisco Jos{\'e} Mart{\'i}nez-Estudillo and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", booktitle = "Proceeding of the Metaheuristics International Conference (MIC 2017) and the XII Metaheur{\'i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2017) Conference", isbn = "978-84-697-4275-1", month = "4th-7th June", organization = "Barcelona", pages = "920-922", publisher = "Universitat Pompeu Fabra", title = "{M}ultiobjective time series segmentation by improving clustering quality and reducing approximation error", url = "www.dropbox.com/s/vzt9wway5knp6ue/Proceedings%20MIC-MAEB%202017.pdf?dl=0", year = "2017", } @conference{Duran-Rosal2017, author = "Antonio Manuel Dur{\'a}n-Rosal and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Time series segmentation can be approached using metaheuristics procedures such as genetic algorithms (GAs) methods, with the purpose of automatically finding segments and determine similarities in the time series with the lowest possible clustering error. In this way, segments belonging to the same cluster must have similar properties, and the dissimilarity between segments of different clusters should be the highest possible. In this paper we tackle a specific problem of significant wave height time series segmentation, with application in coastal and ocean engineering. The basic idea in this case is that similarity between segments can be used to characterise those segments with high significant wave heights, and then being able to predict them. A recently metaheuristic, the Coral Reef Optimization (CRO) algorithm is proposed for this task, and we analyze its performance by comparing it with that of a GA in three wave height time series collected in three real buoys (two of them in the Gulf of Alaska and another one in Puerto Rico). The results show that the CRO performance is better than the GA in this problem of time series segmentation, due to the better exploration of the search space obtained with the CRO.", booktitle = "14th International Work-Conference on Artificial and Natural Neural Networks (IWANN2017)", doi = "10.1007/978-3-319-59153-7_58", isbn = "978-3-319-59152-0", month = "14th-16th June", organization = "C{\'a}diz (Spain)", pages = "673-684", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science (LNCS)", title = "{A} coral reef optimization algorithm for wave height time series segmentation problems ", url = "dx.doi.org/10.1007/978-3-319-59153-7_58", volume = "10305", year = "2017", } @conference{dorado2017bd, author = "Manuel Dorado-Moreno and Laura Cornejo-Bueno and Pedro Antonio Guti{\'e}rrez and Luis Prieto and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Wind power ramp events (WPREs) are strong increases or decreases of wind speed in a short period of time. Predicting WPREs is of vital importance given that they can damage the turbines in a wind farm. In contrast to previous binary approaches (ramp versus non-ramp), a three-class prediction is proposed in this paper by considering: negative ramp, non-ramp and positive ramp, where the natural order of the events is clear. The independent variables used for prediction include past ramp function values and meteorological data obtained from physical models (reanalysis data). The proposed methodology is based on reservoir computing and an over-sampling process for alleviating the high degree of unbalance of the dataset (non-ramp events are much more frequent than ramps). The reservoir computing model is a modified echo state network composed by: a recurrent neural network layer, a nonlinear kernel mapping and an ordinal logistic regression, in such a way that the order of the classes can be exploited. The standard synthetic minority oversampling technique (SMOTE) is applied to the reservoir activations, given that the direct application over the input variables would damage its temporal structure. The performance of this proposal is compared to the original dataset (without over-sampling) and to nominal logistic regression, and the results obtained with the oversampled dataset and ordinal logistic regression are found to be more robust.", booktitle = "14th International Work-Conference on Artificial and Natural Neural Networks (IWANN2017)", doi = "10.1007/978-3-319-59153-7_61", isbn = "978-3-319-59152-0", month = "14th-16th June", organization = "C{\'a}diz, Spain", pages = "708-719", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science (LNCS)", title = "{C}ombining {R}eservoir {C}omputing and {O}ver-{S}ampling for {O}rdinal {W}ind {P}ower {R}amp {P}rediction", url = "dx.doi.org/10.1007/978-3-319-59153-7_61", volume = "10305", year = "2017", } @conference{Camacho-CañamónJOVENES-UCO2016, author = "Julio Camacho-Ca{\~n}am{\'o}n", abstract = "Este trabajo pretende resolver un problema existente en el campo de an{\'a}lisis de im{\'a}genes m{\'e}dicas para el diagn{\'o}stico en modelos animales. A partir de im{\'a}genes de resonancia magn{\'e}tica, es necesario calcular el porcentaje de volumen que ocupa una lesi{\'o}n cerebral causada por isquemia respecto al volumen del cerebro. El objetivo de este estudio es automatizar el proceso de c{\'a}lculo, que actualmente se realiza de forma manual, y reducir el tiempo necesario para ello, adem{\'a}s de garantizar la objetividad de los resultados. Aplicando hibridaciones entre algoritmos de visi{\'o}n artificial y de aprendizaje autom{\'a}tico, ha sido posible igualar en precisi{\'o}n y mejorar el tiempo de realizaci{\'o}n de la medida de estos vol{\'u}menes respecto al m{\'e}todo manual actual. Con esto, se concluye que las sinergias que pueden existir entre distintos campos del conocimiento producen resultados interdisciplinares que permiten el avance de la ciencia con la consecuente repercusi{\'o}n sobre el beneficio social.", booktitle = "Investigando por un futuro mejor", isbn = "978-84-9927-291-7", keywords = "Brain volume, contour model, image segmentation, medical image", pages = "196-200", title = "{S}istema autom{\'a}tico para el c{\'a}lculo de volumen de infarto cerebral a partir de im{\'a}genes de resonancia magn{\'e}tica", year = "2016", } @conference{Dueñas-Jurado2016, author = "J.M. Due{\~n}as-Jurado and Pedro Antonio Guti{\'e}rrez and F. Santos-Luna and A. Salvatierra-Vel{\'a}zquez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", booktitle = "XXXI Reuni{\'o}n Nacional de Coordinadores de Transplantes", title = "{N}uevos modelos de asignaci{\'o}n donante-receptor en el trasplante pulmonar", year = "2016", } @conference{DAMA2016Melanoma, author = "Javier S{\'a}nchez-Monedero and Mar{\'i}a P{\'e}rez-Ortiz and A. S{\'a}ez and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Melanoma is a type of cancer that develops from the pigment-containing cells known as melanocytes. Usually occurring on the skin, early detection and diagnosis is strongly related to survival rates. In the present work, we propose a system combining image analysis and machine learning for detecting melanoma presence and severity. The severity is assessed in terms of melanoma thickness, which is measured by the Breslow index. We extract 100 features considering the shape, colour, pigment network and texture of the benign and malignant lesions, tackling this problem as a five-class classification problem, where the first class represents benign lesions and the remaining four classes represent different stages of the melanoma (as measured by the Breslow index). From a machine learning point of view, this problem is a partially ordered classification task. Because of this, we propose specific machine learning models to exploit the partial order information. In this sense, we experimentally demonstrate that specifically designed models achieve better performance than a set of nominal and ordinal classifiers, considering both the imbalanced nature of the problem and the magnitude of the ordinal error in the prediction.", booktitle = "Proceedings of the 1st Workshop on Advances and Applications of Data Science {\&} Engineering", pages = "113-118", title = "{M}achine learning decomposition models for partial ordering problems: {A}n application to melanoma severity classification", year = "2016", } @conference{102016, author = "David Becerra-Alonso and Mariano Carbonero-Ruz and Francisco Fernandez-Navarro", booktitle = "NoLineal 2016. Book of abstracts", organization = "Universidad de Sevilla", pages = "19", series = "Talks, part 1: Theory and Computation", title = "{U}sing {E}xtreme {L}earning {M}achines to cluster supervised data before classification", year = "2016", } @conference{timeDuranSSCI2016, author = "Antonio Manuel Dur{\'a}n-Rosal and Juan Carlos Fern{\'a}ndez and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This work proposes a hybrid methodology for the detection and prediction of Extreme Significant Wave Height (ESWH) periods in oceans. In a first step, wave height time series is approximated by a labeled sequence of segments, which is obtained using a genetic algorithm in combination with a likelihood-based segmentation (GA+LS). Then, an artificial neural network classifier with hybrid basis functions is trained with a multiobjetive evolutionary algorithm (MOEA) in order to predict the occurrence of future ESWH segments based on past values. The methodology is applied to a buoy at the Gulf of Alaska and another one at Puerto Rico. The results show that the GA+LS is able to segment and group the ESWH values, and the neural network models, obtained by the MOEA, make good predictions maintaining a balance between global accuracy and minimum sensitivity for the detection of ESWH events. Moreover, hybrid neural networks are shown to lead to better results than pure models.", booktitle = "2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016)", doi = "10.1109/SSCI.2016.7850144", isbn = "978-1-5090-4240-1", month = "6th-9th December", organization = " Athens, Greece", pages = "1--8", publisher = "IEEE Press", title = "{H}ybridization of neural network models for the prediction of extreme significant wave height segments", url = "dx.doi.org/10.1109/SSCI.2016.7850144", year = "2016", } @conference{162016, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and Mariano Carbonero-Ruz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Learning from label proportions denotes the learning paradigm where training data is provided in groups (or 'bags'), and only the label proportion of each bag is available. The objective is to learn a model to predict the class label of an individual instance, which differentiates this paradigm from the one of multi-instance learning. This learning setting presents very different applications in political science, marketing, healthcare and, in general, all fields concerning anonymous data. Two different iterative strategies are proposed in this paper to deal with this type of problems, both based on the optimisation of the class membership of the patterns using the pattern distribution per bag and the label proportions. To do so, linear discriminant analysis is reformulated to deal with non-crisp class memberships. A thorough set of experiments is conducted to test: 1) the performance gap between these approaches and the fully supervised setting, 2) the potential advantages of optimising class memberships by our proposals, and 3) the influence of factors such as the bag size and the number of classes of the problem in the performance. The results of these experiments are promising, but further research should be encouraged for studying more complex data configurations.", booktitle = "2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016)", doi = "10.1109/SSCI.2016.7850150", isbn = "978-1-5090-4241-8", month = "6th-9th December", organization = "Athens, Greece", pages = "1--7", publisher = "IEEE Press", title = "{A}dapting {L}inear {D}iscriminant {A}nalysis to the {P}aradigm of {L}earning from {L}abel {P}roportions", url = "doi.org/10.1109/SSCI.2016.7850150", year = "2016", } @conference{16, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and J.M. Pe{\~n}a and J. Torres-S{\'a}nchez and F. L{\'o}pez-Granados and C. Herv{\'a}s-Mart{\'i}nez", abstract = "This paper presents a complete system for weed mapping, using imagery provided by unmanned aerial vehicles (UAVs) and machine learning methods. Weed control in precision agriculture designs site-specific control treatments based on weed coverage, where the key is to provide precise weed maps timely. Traditional remote platforms, such as piloted planes and satellites, are, however, not suitable for early weed mapping, given their low spatial and temporal resolutions, as opposed to the ultra-high spatial resolution provided by UAVs. The system here proposed makes use of UAV-imagery and is based on the following steps: 1) Divide the image, 2) compute and binarise the vegetation indexes, 3) detect crop rows, 4) optimise the parameters and 4) learn a classification model. Given that crops are organised in rows, the use of crop rows simplifies the separation between weeds and crops, which is a common handicap given the spectral similitude of both. Different machine learning paradigms are compared in this paper to identify the best suited strategy, including unsupervised, supervised and semi-supervised techniques. Our experiments also study the effect of the flight altitude, the sensor used and the use of previously trained models at a different height. Our results show that 1) very promising performance can be obtained using very few labelled data (although complemented with unlabelled data via the semi-supervised approach) and 2) that the classification model can be learnt in a subplot of the experimental field at low altitude and then applied to the whole field at a higher height, which simplifies the whole process. These results motivate the use of this strategy to design site-specific weed control strategies for early post-emergence weed control.", booktitle = "2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016)", doi = "10.1109/SSCI.2016.7849987", isbn = "978-1-5090-4241-8", month = "6th-9th December", organization = " Athens, Greece", pages = "1--8", publisher = "IEEE Press", title = "{M}achine {L}earning paradigms for {W}eed {M}apping via {U}nmanned {A}erial {V}ehicles", url = "doi.org/10.1109/SSCI.2016.7849987", year = "2016", } @conference{Manolo2016CAEPIA, author = "Manuel Dorado-Moreno and Antonio Manuel Dur{\'a}n-Rosal and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and L. Prieto and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper proposes a reservoir computing architecture for predicting wind power ramp events (WPREs), which are strong increases or decreases of wind speed in a short period of time. This is a problem of high interest, because WPREs increases the maintenance costs of wind farms and hinders the energy production. The standard echo state network architecture is modified by replacing the linear regression used to compute the reservoir outputs by a nonlinear support vector machine, and past ramp function values are combined with reanalysis data to perform the prediction. Another novelty of the study is that we will predict three type of events (negative ramps, non-ramps and positive ramps), instead of binary classification of ramps, given that the type of ramp can be crucial for the correct maintenance of the farm. The model proposed obtains satisfying results, being able to correctly predict around 70% of WPREs and outperforming other models.", booktitle = "Proceedings of the 17th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2016)", doi = "10.1007/978-3-319-44636-3_28", isbn = "978-3-319-44635-6", issn = "0302-9743", month = "14th-16th September", organization = "Salamanca (Spain)", pages = "300-309", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science (LNCS)", title = "{M}ulticlass {P}rediction of {W}ind {P}ower {R}amp {E}vents {C}ombining {R}eservoir {C}omputing and {S}upport {V}ector {M}achines", url = "dx.doi.org/10.1007/978-3-319-44636-3_28", volume = "9868", year = "2016", } @conference{Maria2016CAEPIA, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and Mariano Carbonero-Ruz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Learning from label proportions is the term used for the learning paradigm where the training data is provided in groups (or 'bags'), and only the label proportion for each bag is known. The objective is to learn a model to predict the class labels of individual instances. This paradigm presents very different applications, specially concerning anonymous data. Two different iterative strategies are proposed to deal with this type of problems, both based on optimising the class membership of the instances using the estimated pattern distribution per bag and the label proportions. Discriminant analysis is reformulated to deal with non-crisp class memberships. A thorough set of experiments is conducted to test: (1) the performance gap between these approaches and the fully supervised setting, (2) the potential advantages of optimising class memberships by our proposals, and (3) the influence of factors such as the bag size and the number of classes of the problem in the performance.", booktitle = "Proceedings of the 17th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2016)", doi = "10.1007/978-3-319-44636-3_8", isbn = "978-3-319-44635-6", issn = "0302-9743", month = "Septiembre, 2016", organization = "Salamanca, Spain", pages = "79-88", publisher = "Springer International Publishing", series = "Lecture Notes on Computer Science (LNCS)", title = "{L}earning from {L}abel {P}roportions via an {I}terative {W}eighting {S}cheme and {D}iscriminant {A}nalysis", url = "dx.doi.org/10.1007/978-3-319-44636-3_8", volume = "9868", year = "2016", } @conference{CAEPIA2016Antonio, author = "Antonio Manuel Dur{\'a}n-Rosal and Manuel Dorado-Moreno and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper presents a local search (LS) method based on the beta distribution for time series segmentation with the purpose of correctly representing extreme values of the underlying variable studied. The LS procedure is combined with an evolutionary algorithm (EA) which segments time series trying to obtain a given number of homogeneous groups of segments. The proposal is tested on a real problem of wave height estimation, where extreme high waves are frequently found. The results show that the LS is able to significantly improve the clustering quality of the solutions obtained by the EA. Moreover, the best segmentation clearly groups extreme waves in a separate cluster and characterizes them according to their centroid.", booktitle = "Proceedings of the 17th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2016)", doi = "10.1007/978-3-319-44636-3_39", isbn = "978-3-319-44635-6", issn = "0302-9743", month = "14th-16th September", organization = "Salamanca (Spain)", pages = "418-427", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science (LNCS)", title = "{O}n the {U}se of the {B}eta {D}istribution for a {H}ybrid {T}ime {S}eries {S}egmentation {A}lgorithm", url = "dx.doi.org/10.1007/978-3-319-44636-3_39", volume = "9868", year = "2016", } @conference{1520161, author = "Manuel Dorado-Moreno and Mar{\'i}a P{\'e}rez-Ortiz and Maria Dolores Ayll{\'o}n-Ter{\'a}n and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", booktitle = "11th International Conference on Hybrid Artificial Intelligent Systems (HAIS2016)", doi = "10.1007/978-3-319-32034-2_38", isbn = "978-3-319-32033-5", month = "18th-20th April", organization = "Seville (Spain)", pages = "451-462", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science (LNCS)", title = "{O}rdinal {E}volutionary {A}rtificial {N}eural {N}etworks for {S}olving an {I}mbalanced {L}iver {T}ransplantation {P}roblem", url = "dx.doi.org/10.1007/978-3-319-32034-2_38", volume = "9648", year = "2016", } @conference{Duran16b, author = "Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and Francisco Jos{\'e} Mart{\'i}nez-Estudillo and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", booktitle = "11th International Conference on Hybrid Artificial Intelligent Systems (HAIS2016)", doi = "10.1007/978-3-319-32034-2_14", isbn = "978-3-319-32034-2", month = "18th-20th April", organization = "Sevilla (Spain)", pages = "163-173", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science", title = "{T}ime {S}eries {R}epresentation by a {N}ovel {H}ybrid {S}egmentation {A}lgorithm", url = "dx.doi.org/10.1007/978-3-319-32034-2_14", volume = "9648", year = "2016", } @conference{Perez16b, author = "Mar{\'i}a P{\'e}rez-Ortiz and Mercedes Torres-Jim{\'e}nez and Pedro Antonio Guti{\'e}rrez and Javier S{\'a}nchez-Monedero and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", booktitle = "11th International Conference on Hybrid Artificial Intelligent Systems (HAIS2016)", doi = "10.1007/978-3-319-32034-2_50", isbn = "978-3-319-32033-5", month = "18th-20th April", organization = "Seville (Spain)", pages = "597-608", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science (LNCS)", title = "{F}isher {S}core-{B}ased {F}eature {S}election for {O}rdinal {C}lassification: {A} {S}ocial {S}urvey on {S}ubjective {W}ell-{B}eing", url = "dx.doi.org/10.1007/978-3-319-32034-2_50", volume = "9648", year = "2016", } @conference{Sanchez16b, author = "Javier S{\'a}nchez-Monedero and Aurora S{\'a}ez and Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", booktitle = "11th International Conference on Hybrid Artificial Intelligent Systems (HAIS2016)", doi = "10.1007/978-3-319-32034-2_36", isbn = "978-3-319-32033-5", month = "18th-20th April", organization = "Seville (Spain)", pages = "427-438", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science (LNCS)", title = "{C}lassification of {M}elanoma {P}resence and {T}hickness {B}ased on {C}omputational {I}mage {A}nalysis", url = "dx.doi.org/10.1007/978-3-319-32034-2_36", volume = "9648", year = "2016", } @conference{22016, author = "Pedro Antonio Guti{\'e}rrez and Mar{\'i}a P{\'e}rez-Ortiz and Javier S{\'a}nchez-Monedero and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Ordinal input variables are common in many supervised and unsupervised machine learning problems. We focus on ordinal classification problems, where the target variable is also categorical and ordinal. In order to represent these variables for measuring distances or applying continuous mapping functions, they have to be transformed to numeric values. This paper evaluates five different methods to do so. Two of them are commonly applied by practitioners, the first one based on binarising the ordinal input variable using standard indicator variables (NomBin), and the second one based on directly mapping each category to a consecutive natural number (Num). Furthermore, three novel proposals are evaluated in this paper: 1) an ordinal binarisation based on the order of the input variable (OrdBin), 2) the analysis of pairwise distances between input patterns to recover the latent variable generating the ordinal one (NumLVR), and 3) the refinement of the standard numeric transformation by recovering the distance between sets of patterns of consecutive categories (NumCDR). A thorough empirical evaluation is done, considering 12 datasets, 5 performance metrics and 4 classifiers (2 of them of nominal nature and 2 of ordinal nature). The results show that the NomBin representation method leads to the worst results, and that both Num and NumCDR methods obtain very good performance, although NumCDR results are consistently better for almost all performance metrics and classifiers considered.", booktitle = "IEEE International Joint Conference on Neural Networks (IJCNN2016)", doi = "10.1109/IJCNN.2016.7727468", isbn = "978-1-5090-0621-2", month = "24th-29th July", organization = " Vancouver, BC, Canada", pages = "2174-2181", publisher = "IEEE Press", title = "{R}epresenting ordinal input variables in the context of ordinal classification", url = "doi.org/10.1109/IJCNN.2016.7727468", year = "2016", } @conference{1620161, author = "Mar{\'i}a P{\'e}rez-Ortiz and Aurora Saez and Javier S{\'a}nchez-Monedero and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Melanoma is a type of cancer that usually occurs on the skin. Early detection is crucial for ensuring five-year survival (which varies between 15% and 99% depending on the melanoma stage). Melanoma severity is typically diagnosed by invasive methods (e.g. a biopsy). In this paper, we propose an alternative system combining image analysis and machine learning for detecting melanoma presence and severity. The 86 features selected consider the shape, colour, pigment network and texture of the melanoma. As opposed to previous studies that have focused on distinguishing melanoma and non-melanoma images, our work considers a finer-grain classification problem using five categories: benign lesions and 4 different stages of melanoma. The dataset presents two main characteristics that are approached by specific machine learning methods: 1) the classes representing melanoma severity follow a natural order, and 2) the dataset is imbalanced, where benign lesions clearly outnumber melanoma ones. Different nominal and ordinal classifiers are considered, one of them being based on an ordinal cascade decomposition method. The cascade method is shown to obtain good performance for all classes, while respecting and exploiting the order information. Moreover, we explore the alternative of applying a class balancing technique, presenting good synergy with the ordinal and nominal methods.", booktitle = "IEEE International Joint Conference on Neural Networks (IJCNN2016)", doi = "10.1109/IJCNN.2016.7727466", isbn = "978-1-5090-0621-2", month = "24th-29th July", organization = " Vancouver, BC, Canada", pages = "2156-2163", publisher = "IEEE Press", title = "{T}ackling the ordinal and imbalance nature of a melanoma image classification problem", url = "doi.org/10.1109/IJCNN.2016.7727466", year = "2016", } @conference{820151, author = "Mercedes Torres-Jim{\'e}nez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Carlos Garc{\'i}a-Alonso and Salud Mill{\'a}n", booktitle = "Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Articial (CAEPIA 2015)", pages = "92--102", title = "{E}volutionary product unit logistic regression: {T}he case of agrarian efficiency", year = "2015", } @conference{Pilar Gomez-Rey1, author = "Pilar Gomez-Rey and Elena Barbera-Gregori and Francisco Fernandez-Navarro", booktitle = "ICERI 2015: 8th annual International Conference of Education, Research and Innovation", number = "16-18", pages = "8200–8207", title = "{O}peralizionalizing the capability approach in online learning", year = "2015", } @conference{Pilar Gómez-Rey20151, author = "Pilar G{\'o}mez-Rey and Elena Barbera-Gregori and Francisco Fernandez-Navarro", booktitle = "ICDE 2015: The 26th ICDE World Conference", title = "{M}easuring quality in online programs using learners' perceptions", year = "2015", } @conference{hyperchaosDavidCongreso, author = "D. Urzagasti and David Becerra-Alonso and L. M. P{\'e}rez and H. L. Mancini and D. Laroze", abstract = "In the present work we study numerically the deterministic spin dynamics of two interacting anisotropic magnetic particles in the presence of a time dependent external magnetic field. The particles are coupled through their dipole-dipole interaction. The applied magnetic field is composed of a constant amplitude longitudinal component and other transversal with time dependent amplitude. The system is modelled by the dissipative Landau-Lifshitz equation. The different types of synchronisation have been studied finding that the system presents chaotic anti-synchronisation of the canonical component, for a wide range of parameters. Finally, we also found that the system exhibits phase hyper-chaotic synchronisation.", booktitle = "Proceedings of the 3rd Dynamics Days, Springer Proceedings in Physics", doi = "10.1007/978-3-319-24871-4_20", isbn = "978-3-319-24871-4", issn = "0930-8989", keywords = "Nonlinear Dynamics, Materials, Theory and Experiments", month = "3-7 November 2014", organization = " South America, Valparaiso", pages = "261-272", title = "{H}yper-{C}haotic and {C}haotic {S}ynchronisation of {T}wo {I}nteracting {D}ipoles", url = "http://dx.doi.org/10.1007/978-3-319-24871-4_20", volume = "173", year = "2015", } @conference{Pilar Gómez-Rey, author = "Pilar G{\'o}mez-Rey and Francisco Fernandez-Navarro and Elena Barbera-Gregori", booktitle = "Edulearn 2015: 7th annual International Conference on Education and New Learning Technologies", month = "Julio", number = "6-8", pages = "1201–1210", title = "{C}haracterising the success factors on distance education using the {H}ofstede {C}ultural {F}ramework", year = "2015", } @conference{Dorado15c, author = "Manuel Dorado-Moreno and Pedro Antonio Guti{\'e}rrez and Javier S{\'a}nchez-Monedero and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = " This paper proposes a non-linear ordinal logistic regression method based on the combination of a linear regression model and an evolutionary neural network with hybrid basis functions, combining Sigmoidal Unit and Radial Basis Functions neural networks. The process for obtaining the coefficients is carried out in several steps. Firstly we use an evolutionary algorithm to determine the structure of the hybrid neural network model, in a second step we augment the initial feature space (covariate space) adding the non-linear transformations of the input variables given by the hybrid hidden layer of the best individual of the evolutionary algorithm. Finally, we apply an ordinal logistic regression in the new feature space. This methodology is tested using 10 benchmark problems from the UCI repository. The hybrid model outperforms both the RBF and the SU pure models obtaining a good compromise between them and better results in terms of accuracy and ordinal classification error.", booktitle = "16th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2015)", doi = "10.1007/978-3-319-24598-0_27", isbn = "978-3-319-24597-3", month = "09th-12th November", organization = "Albacete (Spain)", pages = "301--311", publisher = "Springer International Publishing", series = " Lecture Notes in Computer Science (LNCS)", title = "{O}vercoming the linearity of {O}rdinal {L}ogistic {R}egression adding non-linear covariates from {E}volutionary {H}ybrid {N}eural {N}etwork models", url = "doi.org/10.1007/978-3-319-24598-0_27", volume = "9422", year = "2015", } @conference{PAGutierrezEnergyFlux2015, author = "Pedro Antonio Guti{\'e}rrez and Juan Carlos Fern{\'a}ndez and Mar{\'i}a P{\'e}rez-Ortiz and Laura Cornejo-Bueno and Enrique Alexandre-Cortizo and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper tackles marine energy prediction from the classification point of view, by previously discretising the real objective variable into a set of consecutive categories or ranges. Given that the range of energy flux is enough to obtain an approximation of the amount of energy produced, the purpose of this discretisation is to simplify the prediction task. A special kind of autoregressive models are considered, where the category to be predicted depends on both the previous values of energy flux and a set of meteorological variables estimated by numerical models. Apart from this, this paper introduces two different ways of adjusting the order of the autoregressive models, one based on nested cross-validation and the other one based on a dynamic window. The results show that these kind of models are able to predict the time series in an acceptable way, and that the dynamic window procedure leads to the best accuracy without needing the additional computational cost of adjusting the order of the model.", booktitle = "13th International Work-Conference on Artificial Neural Networks (IWANN 2015)", doi = "10.1007/978-3-319-19222-2_8", isbn = "978-3-319-19221-5", month = "10th-12th June", organization = "Palma de Mallorca (Spain)", pages = "92-102", series = "Lecture Notes in Computer Science (LNCS)", title = "{E}nergy {F}lux {R}ange {C}lassification by {U}sing a {D}ynamic {W}indow {A}utoregressive {M}odel", url = "http://link.springer.com/chapter/10.1007%2F978-3-319-19222-2_8", volume = "9095", year = "2015", } @conference{UAVandOBIAPerez2015, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and J.M. Pe{\~n}a and J. Torres-S{\'a}nchez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and F. L{\'o}pez Granados", abstract = "Weed control in precision agriculture refers to the design of site-specific control treatments according to weed coverage and it is very useful to minimise costs and environmental risks. The crucial component is to provide precise and timely weed maps via weed monitoring. This paper compares different approaches for weed mapping using imagery from Unmanned Aerial Vehicles in sunflower crops. We explore differ- ent alternatives, such as object-based analysis, which is a strategy that is spreading rapidly in the field of remote sensing. The usefulness of these approaches is tested by considering support vector machines, one of the most popular machine learning classifiers. The results show that the object-based analysis is more promising than the pixel-based one and demonstrate that both the features related to vegetation indexes and those related to the shape of the objects are meaningful for the problem. ", booktitle = "13th International Work-Conference on Artificial Neural Networks (IWANN 2015)", isbn = "978-3-319-19257-4", month = "10th-12th June", organization = "Palma de Mallorca (Spain)", pages = "252--262", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science", title = "{A}n experimental comparison for the identification of weeds in sunflower crops via unmanned aerial vehicles and object-based analysis", url = "http://link.springer.com/chapter/10.1007%2F978-3-319-19258-1_22", volume = "9094", year = "2015", } @conference{DuranIWANN2015, author = "Antonio Manuel Dur{\'a}n-Rosal and M{\'o}nica de la Paz Mar{\'i}n and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Time-series segmentation can be approached by combining a clustering technique and genetic algorithm (GA) with the purpose of automatically finding segments and patterns of a time series. This is an interesting data mining field, but its application to the optimal segmentation of financial time series is a very challenging task, so accurate algorithms are needed. In this sense, GAs are relatively poor at finding the precise optimum solution in the region where the algorithm converges. Thus, this work presents a hybrid GA algorithm including a local search method, aimed to improve the quality of the final solution. The local search algorithm is based on maximizing a likelihood ratio, assuming normality for the series and the subseries in which the original one is segmented. A real-world time series in the Spanish Stock Market field was used to test this methodology.", booktitle = "13th International Work-Conference on Artificial Neural Networks (IWANN 2015)", doi = "0.1007/978-3-319-19222-2_6", isbn = "978-3-319-19221-5", keywords = "Time series segmentation, Hybrid algorithms, Clustering, Spanish stock market index", month = "10th-12th June", organization = "Palma de Mallorca (Spain)", pages = "69--79", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science", title = "{A}pplying a {H}ybrid {A}lgorithm to the {S}egmentation of the {S}panish {S}tock {M}arket {I}ndex {T}ime {S}eries", url = "http://dx.doi.org/10.1007/978-3-319-19222-2_6", volume = "9095", year = "2015", } @conference{Dorado15b, author = "Manuel Dorado-Moreno and Pedro Antonio Guti{\'e}rrez and Javier S{\'a}nchez-Monedero and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper proposes a nonlinear ordinal logistic regression method based on the hybridization of a linear model and radial basis function (RBF) neural network models for ordinal regression. The process for obtaining the coefficients is carried out in several steps. In the first step we use an evolutionary algorithm to determine the structure of the RBF neural network model, in a second step we transform the initial feature space (covariate space) adding the nonlinear transformations of the input variables given by the RBFs of the best individual in the final generation of the evolutionary algorithm. Finally, we apply an ordinal logistic regression in the new feature space. This methodology is tested using 8 benchmark problems from the UCI repository. The hybrid model outperforms both the linear and the nonlinear part obtaining a good compromise between them and better results in terms of accuracy and ordinal classification error.", booktitle = "13th International Work-Conference on Artificial Neural Networks (IWANN 2015)", doi = "10.1007/978-3-319-19222-2_7", isbn = "978-3-319-19221-5", month = "10th-12th June", organization = "Palma de Mallorca (Spain)", pages = "80--91", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science (LNCS)", title = "{N}onlinear {O}rdinal {L}ogistic {R}egression using covariates obtained by {R}adial {B}asis {F}unction neural networks models", url = "http://link.springer.com/chapter/10.1007%2F978-3-319-19222-2_7", volume = "9095", year = "2015", } @conference{salazar2014, author = "M. Salazar-Ord{\'o}{\~n}ez and M. Rodr{\'i}guez-Entrena and David Becerra-Alonso", booktitle = "Proceedings of the EAAI 2014 Congress ‘Agri-Food and Rural Innovations for Healthier Societies’", keywords = "Genetically Modified Food, Willingness to purchase, Artificial Neural Network", month = "August", organization = "Ljubljana, Slovenia", title = "{W}illingness to purchase {G}enetically {M}odified food: an analysis applying artificial {N}eural {N}etworks", url = "https://ideas.repec.org/p/ags/eaae14/182940.html", year = "2014", } @conference{TSSIbex35HAIS, author = "Manuel Cruz-Ram{\'i}rez and M{\'o}nica de la Paz Mar{\'i}n and Mar{\'i}a P{\'e}rez-Ortiz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The discovery of characteristic time series patterns is of fundamental importance in financial applications. Repetitive structures and common type of segments can provide very useful information of patterns in financial time series. In this paper, we introduce a time series segmentation and characterisation methodology combining a maximal likelihood optimisation procedure and a clustering technique to automatically segment common patterns from financial time series and address the problem of stock market prices trends. To do so, the obtained segments are transformed into a five-dimensional space composed of five typical statistical measures in order to group them according to their statistical properties. The experimental results show that it is possible to exploit the behaviour of the stock market Ibex-35 Spanish index (closing prices) to detect homogeneous segments of the time series.", booktitle = "Proceedings of the 9th International Conference on Hybrid Artificial Intelligence Systems (HAIS2014)", doi = "10.1007/978-3-319-07617-1_7", isbn = "978-3-319-07616-4", issn = "0302-9743", keywords = "Clustering,Ibex-35 index,segmentation,stock market,time series", pages = "74--85", publisher = "Springer", series = "Lecture Notes in Computer Science", title = "{T}ime series segmentation and statistical characterisation of the {S}panish stock market {I}bex-35 index", url = "http://dx.doi.org/10.1007/978-3-319-07617-1_7", volume = "8480", year = "2014", } @conference{1620141, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Manifold learning covers those learning algorithms where high-dimensional data is assumed to lie on a low-dimensional manifold (usually nonlinear). Specific classification algorithms are able to preserve this manifold structure. On the other hand, ordinal regression covers those learning problems where the objective is to classify patterns into labels from a set of ordered categories. There have been very few works combining both ordinal regression and manifold learning. Additionally, privileged information refers to some specialfeatures which are available during classifier training, but not in the test phase. This paper contributes a new algorithm for combining ordinal regression and manifold learning, based on the idea of constructing aneighbourhood graph and obtaining the shortest path between all pairs of patterns. Moreover, we proposeto exploit privileged information during graph construction, in order to obtain a better representation of theunderlying manifold. The approach is tested with one synthetic experiment and 5 real ordinal datasets, showinga promising potential.", booktitle = "6th International Conference on Neural Computation Theory and Applications (NCTA2014)", isbn = "978-989-758-054-3", keywords = "Manifold Learning, Ordinal Regression, Privileged Information, Kernel Learning", month = "22th-24th October", organization = "Roma (Italy)", pages = "187-194", publisher = "SCITEPRESS", title = "{I}ncorporating privileged information to improve manifold ordinal regression", url = "http://www.ijcci.org/Program/2014/Program_Friday.htm", year = "2014", } @conference{10571861922014, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper deals with the idea of decomposing ordinal multiclass classification problems when working with kernel methods. The kernel parameters are optimised for each classification subtask in order to better adjust the kernel to the data. More flexible multi-scale Gaussian kernels are considered to increase the goodness of fit of the kernel matrices. Instead of learning independent models for all the subtasks, the optimum convex combination of the kernel matrices is then obtained, leading to a single model able to better discriminate the classes in the feature space. The results of the proposed algorithm shows promising potential for the acquisition of better suited kernels. ", booktitle = "6th International Conference on Neural Computation Theory and Applications (NCTA2014)", isbn = "978-989-758-054-3", keywords = "Kernel Learning, Support Vector Machines, Ordinal Classification, Kernel-target Alignment", month = "22th-24th October", organization = "Roma (Italy)", pages = "218-225", publisher = "SCITEPRESS", title = "{L}earning {K}ernel {L}abel {D}ecompositions for {O}rdinal {C}lassification {P}roblems", url = "www.ijcci.org/Program/2014/Program_Friday.htm", year = "2014", } @conference{TSSofPTPsHAIS, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and Javier S{\'a}nchez-Monedero and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Athanasia Nikolaou and Isabelle Dicaire and Fernandez-Navarro, Francisco", abstract = "Recent studies propose that some dynamical systems, such as climate, ecological and financial systems, among others, present critical transition points named to as tipping points (TP). Climate TPs can severely affect millions of lives on Earth so that an active scientific community is working on finding early warning signals. This paper deals with the segmentation of a paleoclimate time series to find segments sharing common patterns with the purpose of finding one or more kinds of segments corresponding to TPs. Due to the limitations of classical statistical methods, we propose the use of a genetic algorithm to automatically segment the series together with a method to perform time series segmentation comparisons. Without a priori information, the method clusters together most of the TPs and avoids false positives, which is a promising result given the challenging nature of the problem.", booktitle = "9th International Conference on Hybrid Artificial Intelligence Systems (HAIS2014)", doi = "10.1007/978-3-319-07617-1_29", isbn = "978-3-319-07616-4", issn = "0302-9743", keywords = "Time series segmentation, genetic algorithms, clustering, paleoclimate data, tipping points, abrupt climate change", month = "11th-13th September", organization = "Salamanca (Spain)", pages = "318--329", publisher = "Springer", series = "Lecture Notes in Computer Science", title = "{T}ime series segmentation of paleoclimate tipping points by an evolutionary algorithm", url = "http://dx.doi.org/10.1007/978-3-319-07617-1_29", volume = "8480", year = "2014", } @conference{LGDOviaMLHAIS, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Ordinal regression considers classification problems where there exist a natural ordering between the categories. In this learning setting, thresholds models are one of the most used and successful techniques. These models are based on the idea of projecting the patterns to a line, which is thereafter divided into intervals using a set of biases or thresholds. This paper proposes a general likelihood-based optimisation framework to better fit probability distributions for ordered categories. To do so, a specific probability distribution (log-gamma) is used, which generalises three commonly used link functions (log-log, probit and complementary log-log). The experiments show that the methodology is not only useful to provide a probabilistic output of the classifier but also to improve the performance of threshold models when reformulating the prediction rule to take these probabilities into account.", booktitle = "9th International Conference on Hybrid Artificial Intelligence Systems (HAIS2014)", doi = "10.1007/978-3-319-07617-1_40", isbn = "978-3-319-07616-4", issn = "0302-9743", month = "11th-13th June", organization = "Salamanca (Spain)", pages = "454--465", publisher = "Springer", series = "Lecture Notes in Computer Science (LNCS)", title = "{L}og-gamma distribution optimisation via maximum likelihood for ordered probability estimates", url = "http://dx.doi.org/10.1007/978-3-319-07617-1_40", volume = "8480", year = "2014", } @conference{SVORIMP2014, author = "Fengzhen Tang and Peter Tino and Pedro Antonio Guti{\'e}rrez and Huanhuan Chen", abstract = "We introduce a new methodology, called SVORIM+, for utilizing privileged information of the training examples, unavailable in the test regime, to improve generalization performance in ordinal regression. The privileged information is incorporated during the training by modelling the slacks through correcting functions for each of the parallel hyperplanes separating the ordered classes. The experimental results on several benchmark and time series datasets show that inclusion of the privileged information during training can boost the generalization performance significantly.", address = "Bruges (Belgium)", booktitle = "Proceedings of the 2014 European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN2014)", isbn = "978-287419095-7", month = "23th-25th April", organization = "Bruges, Belgium", pages = "253-258", title = "{S}upport {V}ector {O}rdinal {R}egression using {P}rivileged {I}nformation", url = "www.i6doc.com/en/book/?GCOI=28001100432440", year = "2014", } @conference{AEEH2014, author = "Manuel Rodriguez-Per{\'a}lvarez and Manuel Cruz-Ram{\'i}rez and E. Tsochatzis and Carlos Garc{\'i}a-Caparr{\'o}s and Pedro Antonio Guti{\'e}rrez and G. Pieri and Mar{\'i}a P{\'e}rez-Ortiz and J.L. Montero-{\'A}lvarez and A. Poyato and Javier Brice{\~n}o and A. Burroughs and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and de la Mata, Manuel", address = "Madrid, Espa{\~n}a", awards = "JCR(2014): 0.838 Position: 80/84 (Q4) Category: GASTROENTEROLOGY {\&} HEPATOLOGY.", booktitle = "Congreso de la Asociaci{\'o}n Espa{\~n}ola para el Estudio del H{\'i}gado", comments = "JCR(2014): 0.838 Position: 80/84 (Q4) Category: GASTROENTEROLOGY {\&} HEPATOLOGY.", issn = "0210-5705", month = "Febrero, 2014", note = "JCR(2014): 0.838 Position: 80/84 (Q4) Category: GASTROENTEROLOGY {\&} HEPATOLOGY.", number = "37", pages = "94", publisher = "Elsevier, Doyma", series = "Gastroenterologia y Hepatologia", title = "{U}tilidad de los modelos de aprendizaje autom{\'a}tico para la predicci{\'o}n de la recidiva del hepatocarcinoma tras el trasplante hep{\'a}tico ", url = "http://aeeh.es/scheduler/congreso-aeeh-2014/", volume = "37", year = "2014", } @conference{ayllonSETH2013, author = "M. D. Ayll{\'o}n and R. Ciria and R. Valente and Manuel Cruz-Ram{\'i}rez and Mar{\'i}a P{\'e}rez-Ortiz and R. Orti and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and M. Rela and N. Heaton and de la Mata, M. and J. Brice{\~n}o", booktitle = "XXIV Congreso de la Sociedad Espa{\~n}ola de Trasplante Hep{\'a}tico", title = "{V}alidaci{\'o}n externa de un modelo de asignaci{\'o}n de redes neuronales en la asignaci{\'o}n donante-receptor en trasplante hep{\'a}tico", year = "2013", } @conference{KPOMEKM2013, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and Manuel Cruz-Ram{\'i}rez and Javier S{\'a}nchez-Monedero and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The classification of patterns into naturally ordered labels is referred to as ordinal regression. This paper explores the notion of kernel trick and empirical feature space in order to reformulate the most widely used linear ordinal classification algorithm (the Proportional Odds Model or POM) to perform nonlinear decision regions. The proposed method seems to be competitive with other state-of-the-art algorithms and significantly improves the original POM algorithm when using 8 ordinal datasets. Specifically, the capability of the methodology to handle non-linear decision regions has been proven by the use of a non-linearly separable toy dataset. ", booktitle = "12th International Work Conference on Artificial Neural Networks (IWANN2013)", doi = "10.1007/978-3-642-40846-5_47", isbn = "978-3-642-38678-7", keywords = "Proportional Odds Model, Ordinal Regression, Kernel Trick", month = "12th-14th June", organization = "Tenerife, Spain", pages = "270-280", series = "Lecture Notes on Computer Science (LNCS)", title = "{K}ernelizing the {P}roportional {O}dds {M}odel through the {E}mpirical {K}ernel {M}apping", url = "dx.doi.org/10.1007/978-3-642-40846-5_47", volume = "7902", year = "2013", } @conference{SanchezMonedero2013hais, author = "Javier S{\'a}nchez-Monedero and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", booktitle = "8th International Conference on Hybrid Artificial Intelligence Systems (HAIS2013)", doi = "10.1007/978-3-642-40846-5_50", editor = "Pan, Jeng-Shyang and Polycarpou, MariosM. and Woźniak, Michał and Carvalho, Andr{\'e}C.P.L.F. and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio", isbn = "978-3-642-40845-8", month = "11th-13th September", organization = "Salamanca (Spain)", pages = "500-509", publisher = "Springer Berlin Heidelberg", series = "Lecture Notes in Computer Science (LNCS)", title = "{E}volutionary ordinal extreme learning machine", url = "dx.doi.org/10.1007/978-3-642-40846-5_50", volume = "8073", year = "2013", } @conference{ECMRO2013, author = "Pedro Antonio Guti{\'e}rrez and Mar{\'i}a P{\'e}rez-Ortiz and Javier S{\'a}nchez-Monedero and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "En este trabajo se presenta un estudio comparativo de una familia de m{\'e}todos de clasificaci{\'o}n en el contexto de regresi{\'o}n ordinal, los conocidos como m{\'e}todos de umbral. El t{\'e}rmino regresi{\'o}n ordinal hace referencia a aquellos problemas de clasificaci{\'o}n en los que las categor{\'i}as de la variable discreta a predecir guardan una relaci{\'o}n natural de orden. Al contrario que los m{\'e}todos tradicionales de clasificaci{\'o}n nominal que ignoran el orden de las etiquetas, los m{\'e}todos de umbral est{\'a}n dise{\~n}ados para aprovecharlo. En concreto, se basan en proyectar los datos a una l{\'i}nea recta mediante un modelo lineal o no lineal y aprender un conjunto de umbrales que dividen dicha l{\'i}nea en las distintas categor{\'i}as, incluyendo la informaci{\'o}n de orden de forma directa. Aunque la versi{\'o}n lineal de los m{\'e}todos de umbral es bien conocida y utilizada en {\'a}reas como ciencias sociales, su uso no es tan popular en el {\'a}rea de aprendizaje autom{\'a}tico. Sin embargo, existen algunos clasificadores no lineales basados en estas ideas. Este art{\'i}culo introduce las caracter{\'i}sticas de los m{\'e}todos existentes y ofrece un estudio comparativo experimental de los mismos. ", booktitle = "IX Congreso Espa{\~n}ol de Metaheur{\'i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2013)", isbn = "978-84-695-8348-7", month = "17th-20th September", organization = "Madrid, Spain", pages = "872--881", title = "{E}studio comparativo de distintos m{\'e}todos de umbral en regresi{\'o}n ordinal", year = "2013", } @conference{SOEFS2013, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The imbalanced nature of some real-world data is one of the current challenges for machine learning, giving rise to different approaches to handling it. However, preprocessing methods operate in the original input space, presenting distortions when combined with the kernel classifiers, which make use of the feature space. This paper explores the notion of empirical feature space (a Euclidean space which is isomorphic to the feature space) to develop a kernel-based synthetic over-sampling technique, which maintains the main properties of the kernel mapping. The proposal achieves better results than the same oversampling method applied to the original input space. ", address = "Brudge", booktitle = "21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN2013)", doi = "http://www.i6doc.com/en/livre/?GCOI=28001100131010", isbn = "978-2-87419-081-0", month = "24th-25th April", organization = "Bruges, Belgium", pages = "385-390", title = "{S}ynthetic over-sampling in the empirical feature space", url = "https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2013-103.pdf", year = "2013", } @conference{MSKTA2013, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and Javier S{\'a}nchez-Monedero and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The problem considered is the optimization of a multi-scale kernel, where a different width is chosen for each feature. This idea has been barely studied in the literature, and through the use of evolutionary or gradient descent approaches, which explicitly train the learning machine and thereby incur high computacional cost. To cope with this limitation, the problem is explored by making use of an analytical methodology known as kernel-target alignment, where the kernel is optimized by aligning it to the so-called ideal kernel matrix. The results show that the proposal leads to better performance and simpler models at limited computational cost when applying the binary Support Vector Machine (SVM) paradigm. ", address = "Brudge", booktitle = "21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN2013)", doi = "http://www.i6doc.com/en/livre/?GCOI=28001100131010", isbn = "978-2-87419-081-0", month = "24th-25th April", organization = "Bruges, Belgium", pages = "391-396", title = "{M}ulti-scale {S}upport {V}ector {M}achine {O}ptimization by {K}ernel {T}arget-{A}lignment", url = "https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2013-21.pdf", year = "2013", } @conference{BKO2013, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Nowadays, the imbalanced nature of some real-world data is receiving a lot of attention from the pattern recognition and machine learning communities in both theoretical and practical aspects, giving rise to different promising approaches to handling it. However, preprocessing methods operate in the original input space presenting distortions when combined with kernel classifiers, that operate in the feature space induced by a kernel function. This paper explores the notion of empirical feature space (a Euclidean space which is isomorphic to the feature space and therefore preserves its structure) to derive a kernel-based synthetic over-sampling technique based on borderline instances which are considered as crucial for establishing the decision boundary. Therefore, the proposed methodology would maintain the main properties of the kernel mapping while reinforcing the decision boundaries induced by a kernel machine. The results show that the proposal achieves better results than the same borderline over-sampling method applied in the original input space.", booktitle = "8th International Conference on Hybrid Artificial Intelligence Systems (HAIS2013)", doi = "10.1007/978-3-642-40846-5_47", isbn = "978-3-642-40845-8", month = "11th-13th September", organization = "Salamanca (Spain)", pages = "472--481", series = "Lecture Notes on Computer Science (LNCS)", title = "{B}orderline kernel based over-sampling", url = "doi.org/10.1007/978-3-642-40846-5_47", volume = "8073", year = "2013", } @conference{nSSDG2013, author = "Javier S{\'a}nchez-Monedero and Pedro Antonio Guti{\'e}rrez and Mar{\'i}a P{\'e}rez-Ortiz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Synthetic datasets can be useful in a variety of situations, specifically when new machine learning models and training algorithms are developed or when trying to seek the weaknesses of an specific method. In contrast to real-world data, synthetic datasets provide a controlled environment for analysing concrete critic points such as outliers tolerance, data dimensionality influence and class imbalance, among others. In this paper, a framework for synthetic data generation is developed with special attention to patterns ordered in the space, data dimensionality, class overlapping and data multimodality. Variables such as position, width and overlapping of data distributions in the n-dimensional space are controlled by considering them as n-spheres. The method is tested in the context of ordinal regression, a paradigm of classification where there is an order arrangement between categories. The contribution of the paper is the full control over data topology and over a set of relevant statistical properties of the data. ", booktitle = "International Work Conference on Artificial Neural Networks (IWANN 2013)", doi = "10.1007/978-3-642-38679-4_62", isbn = "978-3-642-38678-7", keywords = "synthetic data, data generator, data complexity, ordinal classifica- tion, ordinal regression, experimental design", month = "12th-14th June", organization = "Tenerife, Spain", pages = "613--621", publisher = "Springer-Verlag Berlin Heidelberg", series = "Lecture Notes in Computer Science", title = "{A} n-spheres based synthetic data generator for supervised classification", url = "http://dx.doi.org/10.1007/978-3-642-38679-4_62", volume = "7902", year = "2013", } @conference{MLforCFP2013, author = "Mar{\'i}a P{\'e}rez-Ortiz and R. Colmenarejo and Juan Carlos Fern{\'a}ndez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The overcapacity of the European fishing fleets is one of the recognized factors for the lack of success of the Common Fisheries Policy. Unwanted non-targeted species and other incidental fish likely represent one of the causes for the overexploitation of fish stocks; thus there is a clear connection between this problem and the type of fishing gear used by vessels. This paper performs an environmental impact study of the Spanish Fishing Fleet by means of ordinal classification techniques to emphasize the need to design an effective and differentiated common fish policy for "artisan fleets", that guarantees the maintenance of environmental stocks and the artesan fishing culture. ", booktitle = "International Work Conference on Artificial Neural Networks", keywords = "Machine learning, Ordinal Classification, Commitment to sustainability, Common Fisheries Policy, Fleet Overcapacity", month = "12-14 June", organization = " Puerto de la Cruz, Spain", pages = " 278-286", series = "Advances in Computational Intelligence, Part II, Lecture Notes in Computer Science", title = "{C}an machine learning techniques help to improve the {C}ommon {F}isheries {P}olicy?", volume = "7903", year = "2013", }