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.
This work has been financed in part by TIN 2005-08386-C05-02 projects of the Spanish Inter-Ministerial Commission of Science and Technology (MICYT) and FEDER funds.
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Martínez-Estudillo, F.J., Hervás-Martínez, C., Peña, P.A.G., Martínez-Estudillo, A.C., Ventura-Soto, S. (2006). Evolutionary Product-Unit Neural Networks for Classification. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_157
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DOI: https://doi.org/10.1007/11875581_157
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