An Efficient Feature Selection Using Multi-Criteria in Text Categorization

Authors:
Son Doan;Susumu Horiguchi
Affiliations:
Japan Advance Institute of Science and Technology, Japan;Tohoku University, Japan
Venue:
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Year:
2004

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Abstract

Text categorization is a problem of assigning a document into one or more predefined classes. One of the most interesting issues in text categorization is feature selection. This paper proposes a novel approach in feature selection based on multi-criteria ranking of features. Based on a threshold value for each criterion, a new procedure for feature selection is proposed and applied to a text categorization. Experiments dealing with the Reuters-21578 benchmark data and the naive Bayes algorithm show that the proposed approach outperforms performances in compare to conventional feature selection methods.