Text classification based on the TAN model

Hong Bo Shi; Zhi Hai Wang; Hou Kuan Huang; Li Ping Jing

Text classification based on the TAN model

Hong Bo Shi^*
, Zhi Hai Wang
, Hou Kuan Huang
, Li Ping Jing

^*Corresponding author for this work

Beijing Jiaotong University

Research output: Contribution to conference › Paper › peer-review

7 Scopus citations

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.

Original language	English
Pages	43-46
Number of pages	4
State	Published - 2002
Externally published	Yes
Event	2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering - Beijing, China Duration: 28 Oct 2002 → 31 Oct 2002

Conference

Conference	2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering
Country/Territory	China
City	Beijing
Period	28/10/02 → 31/10/02

Keywords

Bayesian Network
Feature selection
Naive Bayes
TAN
Text classification

Cite this

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title = "Text classification based on the TAN model",

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 = "Bayesian Network, Feature selection, Naive Bayes, TAN, Text classification",

author = "Shi, \{Hong Bo\} and Wang, \{Zhi Hai\} and Huang, \{Hou Kuan\} and Jing, \{Li Ping\}",

year = "2002",

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note = "2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering ; Conference date: 28-10-2002 Through 31-10-2002",

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TY - CONF

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AU - Shi, Hong Bo

AU - Wang, Zhi Hai

AU - Huang, Hou Kuan

AU - Jing, Li Ping

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N2 - 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.

AB - 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.

KW - Bayesian Network

KW - Feature selection

KW - Naive Bayes

KW - TAN

KW - Text classification

UR - https://www.scopus.com/pages/publications/0037999830

M3 - 论文

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T2 - 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering

Y2 - 28 October 2002 through 31 October 2002

ER -

Text classification based on the TAN model

Abstract

Conference

Keywords

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