Title

An Effective Dimension Reduction Approach to Chinese Document Classification using Genetic Algorithm

Abstract

Different kinds of methods have been proposed in Chinese document classification, while high dimension of feature vector is one of the most significant limits in these methods. In this paper, an important difference is pointed out between Chinese document classification and English document classification. Then an efficient approach is proposed to reduce the dimension of feature vector in Chinese document classification using Genetic Algorithm. Through merely choosing the set of much more "important" features, the proposed method significantly reduces the number of Chinese feature words. Experiments combining with several relative studies show that the proposed method has great effect on dimension reduction with little loss in correctly classified rate.

Department(s)

Computer Science

Keywords and Phrases

Chinese Document Classification; Dimension Reduction; Genetic Algorithm (GA); Support Vector Machine(SVM)

International Standard Serial Number (ISSN)

0302-9743

Document Type

Book - Chapter

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2009 Springer, All rights reserved.

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