[1]ZHANG Zhi-fei,MIAO Duo-qian.Feature selection for text categorization based on rough set[J].CAAI Transactions on Intelligent Systems,2009,4(5):453-457.[doi:10.3969/j.issn.1673-4785.2009.05.011]
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Feature selection for text categorization based on rough set

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 4 Number of periods: 2009 5 Page number: 453-457 Column: 学术论文—自然语言处理与理解 Public date: 2009-10-25
Title:
Feature selection for text categorization based on rough set
Author(s):
ZHANG Zhi-fei 1; 2;  MIAO Duo-qian 1; 2
1. Department of Computer Science and Technology, Tongji University, Shanghai 201804, China; 2. The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Shanghai 201804, China
Keywords:
text categorization;  rough set;  feature selection;  quick reduction
CLC:
TP391
DOI:
10.3969/j.issn.1673-4785.2009.05.011
Abstract:
Text categorization assigns text documents to one or more predefined categories based on their contents. This assists content-based information management. A difficult problem in this task is the high dimensionality of the feature space. To resolve this, a feature selection method was employed to reduce the dimensions. A new approach based on rough sets,that we call it the improved quick reduction (IQR) algorithm,was proposed. It involved both attribute reduction and text categorization. The experimental results demonstrated the effectiveness of the proposed algorithm. It reduced the dimensionality of feature space, while maintaining high accuracy.
References:
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