Structure Search and Stability Enhancement of Bayesian Networks

Learning Bayesian network structure from large-scale data sets, without any expertspecified ordering of variables, remains a difficult problem. We propose systematic improvements to automatically learn Bayesian network structure from data. First, we propose a linear parent search method to generate candidate graph. Second, we propose a formal approach to eliminate cycles using minimal likelihood loss, a short cycle first heuristic, and a cut-edge repairing. Third, we propose structure perturbation to assure the stability of the network. This step also suggests a stabilityenhancement method to refine the network structure. The algorithms are easy to implement and efficient for large networks. Experimental results on two data sets show that our new approach outperforms existing methods.

• Type: Conference
• Year: 2003
• Where: ICDM
• Authors: Hanchuan Peng, Chris H. Q. Ding
• Keywords: Bayesian Network Structure, Data Mining, Data Sets, ICDM 2003, Learning Bayesian Network Structure