Input selection by multilayer feedforward trained networks

We review feature selection methods based on the analysis of a trained multilayer feedforward neural network. Furthermore, we present a methodology that allows experimentally evaluating and comparing feature selection methods. This methodology was applied to the 19 reviewed methods and we evaluated the usefulness of these methods for selecting the appropriate features in the case of using a multilayer feedforward as a pattern recognition method. We used a total number of 15 different real world classification problems in our experiments. From the result of the comparison, we conclude which methods perform better and should be used, and discuss their applicability