We present a complete review of feature selection methods based on an analysis of the training set. The focus is on the methods which have been applied to neural networks. We also present a methodology that allows evaluating and comparing feature selection methods. This methodology is applied to the 7 reviewed methods in a total of 15 different real world classification problems. The result is an ordination of methods according to its performance. From this ordination it is clearly concluded which method is the best and should be used. The best methods are based on information theory concepts like gd-distance and mutual information. We also discuss the applicability and computational complexity of the methods.
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