Evolutionary product-unit neural networks classifiers

Abstract

In this paper we propose a classification method based on a special class of feed-forward neural network, namely product-unit neural networks. Product-units 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.