document: Feature selection methods for text classification We consider feature selection for text classification both theoretically and empirically . Our main result is an unsupervised feature selection strategy for which we give worst-case theoretical guarantees on the generalization power of the resultant classification function f with respect to the classification function f obtained when keeping all the features . To the best of our knowledge , this is the first feature selection method with such guarantees . In addition , the analysis leads to insights as to when and why this feature selection strategy will perform well in practice . We then use the TechTC-100 , 20-Newsgroups , and Reuters-RCV2 data sets to evaluate empirically the performance of this and two simpler but related feature selection strategies against two commonly-used strategies . Our empirical evaluation shows that the strategy with provable performance guarantees performs well in comparison with other commonly-used feature selection strategies . In addition , it performs better on certain datasets under very aggressive feature selection .
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