Improving Automatic Text Classification by Integrated Feature Analysis

Lazaro S.P. BUSAGALA  Wataru OHYAMA  Tetsushi WAKABAYASHI  Fumitaka KIMURA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E91-D   No.4   pp.1101-1109
Publication Date: 2008/04/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Pattern Recognition
Keyword: 
text classification/categorization,  feature transformation,  dimension reduction,  principal component analysis,  canonical discriminant analysis,  integrated feature analysis,  multiple feature integration,  

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Summary: 
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.