NDLI: Ranking and selecting terms for text categorization via SVM discriminate boundary

Content Provider	IEEE Xplore Digital Library
Author	Tien-Fang Kuo Yajima, Y.
Copyright Year	2005
Description	Author affiliation: Dept. of Ind. Eng. & Manage., Tokyo Inst. of Technol., Japan (Tien-Fang Kuo; Yajima, Y.)
Abstract	The problem of natural language document categorization consists in classifying documents into predetermined categories based on their contents. Each distinct term, or word, in the documents is a feature for representing a document. In general, the number of terms may be extremely large and the dozens of redundant terms may be included, which may deteriorate the performance of classification. In this paper, an SVM based feature ranking and selecting method for text categorization is proposed. The contribution of each term for classification is calculated based on the nonlinear discriminate boundary generated by support vector machine (SVM). The results of experiments on the Reuters-21S78 dataset show that the proposed method achieves higher classification performance than existing feature selection based on LSI and x/sup 2/ statistics values.
Sponsorship	IEEE Comput. Intelligence Soc
Starting Page	496
Ending Page	501
File Size	2536606
Page Count	6
File Format	PDF
ISBN	0780390172
DOI	10.1109/GRC.2005.1547341
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Content management Technology management Engineering management Text categorization Natural languages Support vector machine classification Industrial engineering Large scale integration Statistics
Content Type	Text
Resource Type	Article

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