Learning Bayesian networks with combination of MRMR criterion and EMI method

Authors:
Fengzhan Tian;Feng Liu;Zhihai Wang;Jian Yu
Affiliations:
School of Computer & Information Technology, Beijing Jiaotong University, Beijing, China;School of Computer science & Technology, Beijing University of Posts and Telecommunications, Beijing, China;School of Computer & Information Technology, Beijing Jiaotong University, Beijing, China;School of Computer & Information Technology, Beijing Jiaotong University, Beijing, China
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

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Abstract

Currently, learning Bayesian Networks (BNs) from data has become a much attention-getting issue in fields of machine learning and data mining. While there exists few efficient algorithms for learning BNs in presence of incomplete data. In this paper, we present a scoring function based on mutual information for evaluating BN structures. To decrease computational complexity, we introduce MRMR criterion into the scoring function, which enables the computation of the scoring function to involve in only two-dimensional mutual information. When the dataset is incomplete, we use EMI method to estimate the Mutual Information (MI) from the incomplete dataset. As for whether a node ordering is manually given or not, we develop two versions of algorithms, named as MRMR- E1 and MRMR-E2 respectively and evaluate them through experiments. The experimental results on Alarm network show good accuracy and efficiency of our algorithms.