This session has been provisionally accepted for the IJCNN 2016 conference which will be held as part of the 2016 IEEE WCCI in Vancouver, Canada at July 25-29, 2016.
Ordinal regression (or ordinal classification) is a relatively new learning problem, where the objective is to learn a rule to predict labels in an ordinal scale - discrete labels endowed with a natural order. Consider, for example, the case of a teacher who rates student's performance using A, B, C, D and E, and A>B>C>D>E. Such order information could be helpful for constructing more robust and fair classifiers and evaluation metrics. On the other hand, ranking generally refers to those problems where the algorithm is given a set of ordered labels, and the objective is to learn a rule to rank patterns by using this discrete set of labels. Many real problems exhibit this structure, e.g. multi-criteria decision making, medicine, risk analysis, university ranking, information retrieval and filtering...
Specific solutions have been recently proposed in the machine learning and pattern recognition literature for both ordinal regression and ranking problems, resulting in a very active research field. This special session aims to cover a wide range of approaches and recent advances in ordinal regression and ranking. We hope that this session can provide a common forum for researchers and practitioners to exchange their ideas and report their latest findings in the area.
In particular we encourage submissions addressing the following issues:
- Extensions of standard classification methods to ordinal regression (Support Vector Machines, Gaussian Processes, Discriminant Analysis, etc.).
- Threshold models for ordinal regression.
- Decomposition methods for ordinal regression and ranking.
- Imbalanced ordinal regression problems.
- Clustering and pre-processing methods for ordinal and monotonic data (data cleaning techniques, feature selection, etc).
- Evaluation measures for ordinal regression and ranking.
- Learning to rank: ranking (pointwise, pairwise and listwise algorithms), sorting and multipartite ranking.
- Monotonic classification methods.
- Preference learning.
- Applications in medicine, information retrieval, recommendation systems, risk analysis… and any other real-world problems.
If you are interested in taking part on this special session, please submit your paper directly through the WCCI website selecting the following option for the "Main research topic": "Ordinal Regression and Ranking". You can find further information related to the submission process and important dates at the conference website.
Papers submitted for special sessions are to be peer-reviewed with the same criteria used for the rest of contributed papers. As a result, all accepted papers will be included in the proceedings of IJCNN2016.