Evolutionary product-unit neural networks for classification

Authors:
F. J. Martínez-Estudillo;C. Hervás-Martínez;P. A. Gutiérrez Peña;A. C. Martínez-Estudillo;S. Ventura-Soto
Affiliations:
Department of Management and Quantitative Methods, ETEA, Córdoba, Spain;Department of Computing and Numerical Analysis of the, University of Córdoba, Campus de Rabanales, Córdoba, Spain;Department of Computing and Numerical Analysis of the, University of Córdoba, Campus de Rabanales, Córdoba, Spain;Department of Management and Quantitative Methods, ETEA, Córdoba, Spain;Department of Computing and Numerical Analysis of the, University of Córdoba, Campus de Rabanales, Córdoba, Spain
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

Citing 6
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Abstract

We propose a classification method based on a special class of feed-forward neural network, namely product-unit neural networks. They are based on multiplicative nodes instead of additive ones, where the nonlinear basis functions express the possible strong interactions between variables. We apply an evolutionary algorithm to determine the basic structure of the product-unit model and to estimate the coefficients of the model. We use softmax transformation as the decision rule and the cross-entropy error function because of its probabilistic interpretation. The empirical results over four benchmark data sets show that the proposed model is very promising in terms of classification accuracy and the complexity of the classifier, yielding a state-of-the-art performance.