Improving the Accuracy of a Two-Stage Algorithm in Evolutionary Product Unit Neural Networks for Classification by Means of Feature Selection

This paper introduces a methodology that improves the accuracy of a two-stage algorithm in evolutionary product unit neural networks for classification tasks by means of feature selection. A couple of filters have been taken into consideration to try out the proposal. The experimentation has been carried out on seven data sets from the UCI repository that report test mean accuracy error rates about twenty percent or above with reference classifiers such as C4.5 or 1-NN. The study includes an overall empirical comparison between the models obtained with and without feature selection. Also several classifiers have been tested in order to illustrate the performance of the different filters considered. The results have been contrasted with nonparametric statistical tests and show that our proposal significantly improves the test accuracy of the previous models for the considered data sets. Moreover, the current proposal is much more efficient than a previous methodology developed by us; lastly, the reduction percentage in the number of inputs is above a fifty five, on average.