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

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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.