Accuracy Increase on Evolving Product Unit Neural Networks via Feature Subset Selection

Abstract

A framework that combines feature selection with evolution ary artificial neural networks is presented. This paper copes with neural networks that are applied in classification tasks. In machine learning area, feature selection is one of the most common techniques for pre processing the data. A set of filters have been taken into consideration to assess the proposal. The experimentation has been conducted on nine data sets from the UCI repository that report test error rates about fif teen percent or above with reference classifiers such as C4.5 or 1-NN. The new proposal significantly improves the baseline framework, both approaches based on evolutionary product unit neural networks. Also several classifiers have been tried in order to illustrate the performance of the different methods considered.