A novel ordering-based Greedy Bayesian network learning algorithm on limited data

Authors:
Feng Liu;Fengzhan Tian;Qiliang Zhu
Affiliations:
Department of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China;Department of Computer Science, Beijing Jiaotong University, Beijing, China;Department of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
Venue:
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Year:
2007

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Abstract

Existing algorithms for learning Bayesian network (BN) require a lot of computation on high dimensional itemsets, which affects accuracy especially on limited datasets and takes up a large amount of time. To alleviate the above problem, we propose a novel BN learning algorithm OMRMRG, Ordering-based Max Relevance and Min Redundancy Greedy algorithm. OMRMRG presents an ordering-based greedy search method with a greedy pruning procedure, applies Max-Relevance and Min-Redundancy feature selection method, and proposes Local Bayesian Increment function according to Bayesian Information Criterion (BIC) formula and the likelihood property of overfitting. Experimental results show that OMRMRG algorithm has much better efficiency and accuracy than most of existing BN learning algorithms on limited datasets.