Cascaded feature selection in SVMs text categorization

Authors:
Takeshi Masuyama;Hiroshi Nakagawa
Affiliations:
Information Technology Center, The University of Tokyo, Bunkyo, Tokyo, Japan;Information Technology Center, The University of Tokyo, Bunkyo, Tokyo, Japan
Venue:
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Year:
2003

Citing 3
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Abstract

This paper investigates the effect of a cascaded feature selection (CFS) in SVMs text categorization. Unlike existing feature selections, our method (CFS) has two advantages. One can make use of the characteristic of each feature (word). Another is that unnecessary test documents for a category, which should be categorized into a negative set, can be removed in the first step. Compared with the method which does not apply CFS, our method achieved good performance especially about the categories which contain a small number of training documents.