A Smart Design for the TAN Classifier

Author

Shao, Lujie ; Wang, Zhihai ; Wang, Shiqiang

Author_Institution

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

fYear

2009

fDate

23-25 Jan. 2009

Firstpage

72

Lastpage

75

Abstract

The naive Bayesian classifier is widely used because of itpsilas simplicity and effectiveness. But it has a strict assumption about the independence for each attribute, which is not obviously hold in real world domains. Many efforts have been made to relax the independence and improve the performance of the naive Bayesian classifier. Tree Augmented Naive Bayes (TAN) classifier was proved to be one of the best methods. In this paper, we analyze the implementations of distribution-based TAN classifier and the classification-based TAN classifier. Then we utilize the information theory to compute the influence between two attributes, and then proposed a new heuristic searching measurement for the tree structure. The experimental results have shown the advantage of the new classifier.

Keywords

Bayes methods; pattern classification; trees (mathematics); TAN classifier; heuristic searching; smart design; tree augmented naive Bayes classifier; Bayesian methods; Classification tree analysis; Data mining; Information technology; Information theory; Mutual information; NP-hard problem; Probability; Tree data structures; Tree graphs; Bayesian classifier; TAN; heuristic searching; information theory; structure learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge Discovery and Data Mining, 2009. WKDD 2009. Second International Workshop on

Conference_Location

Moscow

Print_ISBN

978-0-7695-3543-2

Type

conf

DOI

10.1109/WKDD.2009.97

Filename

4771881