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JACIII Vol.9 No.5 pp. 570-575
doi: 10.20965/jaciii.2005.p0570
(2005)

Paper:

Improving Text Categorization by Multicriteria Feature Selection

Son Doan*, and Susumu Horiguchi**

*Graduate School of Information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan

**Graduate School of Information Science, Tohoku University, 6-3-09 Aoba, Sendai 980-8579, Japan

Received:
October 28, 2004
Accepted:
May 9, 2005
Published:
September 20, 2005
Keywords:
feature selection, text categorization, machine learning, text mining, text representation
Abstract

Text categorization involves assigning a natural language document to one or more predefined classes. One of the most interesting issues is feature selection. We propose an approach using multicriteria ranking of eatures, a new procedure for feature selection, and apply these to text categorization. Experimental results dealing with Reuters-21578 and 20Newsgroups benchmark data and the naive Bayes algorithm show that our proposal outperforms conventional feature selection in text categorization performance.

Cite this article as:
Son Doan and Susumu Horiguchi, “Improving Text Categorization by Multicriteria Feature Selection,” J. Adv. Comput. Intell. Intell. Inform., Vol.9, No.5, pp. 570-575, 2005.
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Last updated on Oct. 15, 2021