New methods for text categorization based on a new feature selection method and a new similarity measure between documents

被引：0

作者：

Lee, Li-Wei ^{[1
]}

Chen, Shyi-Ming ^{[1
]}

机构：

[1] Natl Taiwan Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei, Taiwan

来源：

ADVANCES IN APPLIED ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2006年 / 4031卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we present a new feature selection method based on document frequencies and statistical values. We also present a new similarity measure to calculate the degree of similarity between documents. Based on the proposed feature selection method and the proposed similarity measure between documents, we present three methods for dealing with the Reuters-21578 top 10 categories text categorization. The proposed methods get higher performance for dealing with the Reuters-21578 top 10 categories text categorization than that of the method presented in [4].

引用

页码：1280 / 1289

页数：10

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