Text classification based on the TAN model

Author

Hong-Bo, SHI ; Zhi-Hai, Wang ; Hou-Kuan, Huang ; Li-Ping, Jing

Author_Institution

Sch. of Comput. & Inf. Technol., Northern Jiaotong Univ., Beijing, China

Volume

1

fYear

2002

fDate

28-31 Oct. 2002

Firstpage

43

Abstract

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.

Keywords

belief networks; data mining; feature extraction; learning (artificial intelligence); Bayesian network; Naive Bayesian classifier; TAN model; data mining; feature selection; machine; text classification; tree augmented Naive Bayes method; Bayesian methods; Data mining; Distributed computing; Machine learning; NP-complete problem; Probability distribution; Supervised learning; Testing; Text categorization;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON '02. Proceedings. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering

Print_ISBN

0-7803-7490-8

Type

conf

DOI

10.1109/TENCON.2002.1181210

Filename

1181210