Bayesian Network Structure Ensemble Learning

Authors:
Feng Liu;Fengzhan Tian;Qiliang Zhu
Affiliations:
Department of Computer Science, Beijing University of Posts and Telecommunications, Xitu Cheng Lu 10, 100876 Beijing, China;Department of Computer Science, Beijing Jiaotong University, Shangyuan Cun 3, 100044 Beijing, China;Department of Computer Science, Beijing University of Posts and Telecommunications, Xitu Cheng Lu 10, 100876 Beijing, China
Venue:
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Year:
2007

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Abstract

Bayesian networks (BNs) have been widely used for learning model structures of a domain in the area of data mining and knowledge discovery. This paper incorporates ensemble learning into BN structure learning algorithms and presents a novel ensemble BN structure learning approach. Based on the Markov condition and the faithfulness condition of BN structure learning, our ensemble approach proposes a novel sample decomposition technique and a components integration technique. The experimental results reveal that our ensemble BN structure learning approach can achieve an improved result compared with individual BN structure learning approach in terms of accuracy.