Text claslsification based on the TAN Model

被引：3

作者：

Shi, HB ^{[1
]}

Wang, ZH ^{[1
]}

Huang, HK ^{[1
]}

Jing, LP ^{[1
]}

机构：

[1] No Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China

来源：

2002 IEEE REGION 10 CONFERENCE ON COMPUTERS, COMMUNICATIONS, CONTROL AND POWER ENGINEERING, VOLS I-III, PROCEEDINGS | 2002年

关键词：

text classification; TAN; naive Bayes; Bayesian network; feature selection;

D O I：

10.1109/TENCON.2002.1181210

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes a text classification method based on TAN model. Naive Bayesian classifier is the most effective and popular. text classification method, but its attribute independence assumption makes it unable to express the dependence among text terms. TAN (Tree Augmented Naive Bayes) combines the simplicity of Naive Bayesian with the ability to express the dependence among attributes in Bayesian network. This paper reviews some existing text methods, introduces TAN model, and applies TAN model to text classification. Naive Bayesian and TAN classifiers are also compared by our experiments. Experimental results show TAN classifier has better performance.

引用

页码：43 / 46

页数：4

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