Ordinal Classification Using Hybrid Artificial Neural Networks with Projection and Kernel Basis Functions

Áreas de investigación:

• Methodology - Machine learning

Año:

2012

Tipo de publicación:

Artículo en conferencia

Palabras clave:

Ordinal Classification, Projection basis functions, Kernel basis functions, Evolutionary neural networks, Evolutionary algorithm, Gradient-descent algorithm

Autores:

• Dorado-Moreno, Manuel
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César

Título del libro:

7th International Conference on Hybrid Artificial Intelligence Systems (HAIS2012)

Páginas:

319–330

BibTex:

@conference{DoradoNeuralNetwork2012, author = "Manuel Dorado-Moreno and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Many real life problems require the classification of items into naturally ordered classes. These problems are traditionally handled by conventional methods intended for the classification of nominal classes, where the order relation is ignored. This paper proposes a hybrid neural network model applied to ordinal classification using a possible combination of projection functions (product unit, PU) and kernel functions (radial basis function, RBF) in the hidden layer of a feed-forward neural network. A combination of an evolutionary and a gradient-descent algorithms is adapted to this model and applied to obtain an optimal architecture, weights and node typology of the model. This combined basis function model is compared to the corresponding pure models: PU neural network, and the RBF neural network. Combined functions using projection and kernel functions are found to be better than pure basis functions for the task of ordinal classification in several datasets. ", booktitle = "7th International Conference on Hybrid Artificial Intelligence Systems (HAIS2012)", doi = "10.1007/978-3-642-28931-6_31", keywords = "Ordinal Classification, Projection basis functions, Kernel basis functions, Evolutionary neural networks, Evolutionary algorithm, Gradient-descent algorithm", pages = "319–330", title = "{O}rdinal {C}lassification {U}sing {H}ybrid {A}rtificial {N}eural {N}etworks with {P}rojection and {K}ernel {B}asis {F}unctions", url = "http://dx.doi.org/10.1007/978-3-642-28931-6_31", year = "2012", }

Abstract:

Many real life problems require the classification of items into naturally ordered classes. These problems are traditionally handled by conventional methods intended for the classification of nominal classes, where the order relation is ignored. This paper proposes a hybrid neural network model applied to ordinal classification using a possible combination of projection functions (product unit, PU) and kernel functions (radial basis function, RBF) in the hidden layer of a feed-forward neural network. A combination of an evolutionary and a gradient-descent algorithms is adapted to this model and applied to obtain an optimal architecture, weights and node typology of the model. This combined basis function model is compared to the corresponding pure models: PU neural network, and the RBF neural network. Combined functions using projection and kernel functions are found to be better than pure basis functions for the task of ordinal classification in several datasets.