Abstract
Learning Bayesian network structure from large-scale data sets, without any expert-specified ordering of variables, remains a difficult problem. We propose systematic improvements to automatically learn Bayesian network structure from data. (1) We propose a linear parent search method to generate candidate graph. (2) We propose a comprehensive approach to eliminate cycles using minimal likelihood loss, a short cycle first heuristic, and a cut-edge repairing. (3) We propose structure perturbation to assess the stability of the network and a stability-improvement 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.