Volume 5 Number 11 (Nov. 2010)
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JCP 2010 Vol.5(11): 1755-1761 ISSN: 1796-203X
doi: 10.4304/jcp.5.11.1755-1761

An Improved IAMB Algorithm for Markov Blanket Discovery

Yishi Zhang1, Zigang Zhang2, Kaijun Liu2, and Gangyi Qian3
1 1School of Software Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
2 School of Management, Huazhong University of Science and Technology, Wuhan, 430074, China
3 School of Public Administration, Huazhong University of Science and Technology, Wuhan, 430074, China


Abstract—Finding an efficient way to discover Markov blanket is one of the core issues in data mining. This paper first discusses the problems existed in IAMB algorithm which is a typical algorithm for discovering the Markov blanket of a target variable from the training data, and then proposes an improved algorithm λ-IAMB based on the improving approach which contains two aspects: code optimization and the improving strategy for conditional independence testing. Experimental results show that λ- IAMB algorithm performs better than IAMB by finding Markov blanket of variables in typical Bayesian network and by testing the performance of them as feature selection method on some well-known real world datasets.

Index Terms—data mining, classification, feature selection, Markov blanket, IAMB algorithm

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Cite: Yishi Zhang, Zigang Zhang, Kaijun Liu, and Gangyi Qian, " An Improved IAMB Algorithm for Markov Blanket Discovery," Journal of Computers vol. 5, no. 11, pp. 1755-1761, 2010.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Monthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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