Classification by means of evolutionary product-unit neural networks

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 among the variables. We apply an evolutionary algorithm to determine the basic structure of the product-unit model and to estimate the coefficients of the model. The empirical results show that the proposed model is very promising in terms of classification accuracy, yielding a state-of-the-art performance.