A comparison among feature selection methods based on trained networks

We present a review of feature selection methods, based on the analysis of a trained multilayer feedforward network, which have been applied to neural networks. Furthermore, a methodology that allows evaluating and comparing feature selection methods is carefully described. This methodology is applied to the 19 reviewed methods in a total of 15 different real world classification problems. We present an ordination of methods according to their performance and it is clearly concluded which method performs better and should be used. We also discuss the applicability and computational complexity of the methods