Parallel learning of bayesian networks based on ordering of sets
ASIAN'05 Proceedings of the 10th Asian Computing Science conference on Advances in computer science: data management on the web
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When learning Bayesian networks from data, in many real circumstances, experts of domains could give the relationships between the classes of variables. In this paper, the problem of 'learning Bayesian networks based on ordering of sets' is formulated. To solve this problem, we propose a partitioned greedy search algorithm for learning structures of Bayesian networks based on the ordering of sets. The results of experiments show that, with the ordering of sets, compared with traditional greedy DAG search algorithm, the score of the structure obtained by our algorithm is improved, and the search time is greatly reduced.