Evolutionary product-unit neural networks classifiers

This paper proposes 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. The approach can be seen as nonlinear multinomial logistic regression where the parameters are estimated using evolutionary computation. The empirical and specific multiple comparison statistical test results, carried out over several benchmark data sets and a complex real microbial Listeria growth/no growth problem, show that the proposed model is promising in terms of its classification accuracy and the number of the model coefficients, yielding a state-of-the-art performance.