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• W2526606832 abstract "We consider feature selection for text classification both the- oretically and empirically. Our main result is an unsuper- vised 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 classi- fication function f obtained when keeping all the features. To the best of our knowledge, this is the first feature selec- tion method with such guarantees. In addition, the anal- ysis leads to insights as to when and why this feature se- lection 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 per- forms well in comparison with other commonly-used feature selection strategies. In addition, it performs better on cer- tain datasets under very aggressive feature selection." @default.
• W2526606832 created "2016-10-07" @default.
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• W2526606832 date "2007-01-01" @default.
• W2526606832 modified "2023-10-18" @default.
• W2526606832 title "Feature Selection Methods for Text ClassiÞcation" @default.
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