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IEICE Transactions on Information and Systems 2008 E91-D(4):1101-1109; doi:10.1093/ietisy/e91-d.4.1101
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Copyright © 2008 The Institute of Electronics, Information and Communication Engineers

Regular Section -- Papers -- Pattern Recognition

Improving Automatic Text Classification by Integrated Feature Analysis

Lazaro S.P. BUSAGALA1, Wataru OHYAMA1, Tetsushi WAKABAYASHI1 and Fumitaka KIMURA1

1 The authors are with the Graduate School of Engineering, Mie University, Tsu-shi, 514–8507, Japan. E-mail: busagala{at}hi.info.mie-u.ac.jp; ohyama{at}hi.info.mie-u.ac.jp; waka{at}hi.info.mie-u.ac.jp; kimura{at}hi.info.mie-u.ac.jp


  Abstract

Feature transformation in automatic text classification (ATC) can lead to better classification performance. Furthermore dimensionality reduction is important in ATC. Hence, feature transformation and dimensionality reduction are performed to obtain lower computational costs with improved classification performance. However, feature transformation and dimension reduction techniques have been conventionally considered in isolation. In such cases classification performance can be lower than when integrated. Therefore, we propose an integrated feature analysis approach which improves the classification performance at lower dimensionality. Moreover, we propose a multiple feature integration technique which also improves classification effectiveness.

Key Words: text classification/categorization, feature transformation, dimension reduction, principal component analysis, canonical discriminant analysis, integrated feature analysis, multiple feature integration

Manuscript received June 19, 2007. Manuscript revised November 7, 2007.


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