A modeling approach to feature selection

An information-theoretic approach is used to derive a new feature selection criterion capable of detecting features that are totally useless. Since the number of useless features is initially unknown, traditional class-separability and distance measures are not capable of coping with this problem. The useless feature-subset is detected by fitting a probability model to a given training set of classified feature-vectors using the minimum-description-length criterion (MDLC) for model selection. The resulting criterion for the Gaussian case is a simple closed-form expression, having a plausible geometric interpretation, and is proved to be consistent, i.e., it yields the true useless subset with probability 1 as the size of the training set grows to infinity. Simulations show excellent results compared to the cross-validation method and other information-theoretic criteria, even for small-sized training sets