An improved 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:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

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Abstract

Although encouraging results have been reported, existing Bayesian network (BN) learning algorithms have some troubles on limited data. A statistical or information theoretical measure or a score function may be unreliable on limited datasets, which affects learning accuracy. To alleviate the above problem, we propose a novel BN learning algorithm MRMRG, Max Relevance and Min Redundancy Greedy algorithm. MRMRG algorithm applies Max Relevance and Min Redundancy feature selection technique and proposes Local Bayesian Increment (LBI) function according to the Bayesian Information Criterion (BIC) formula and the likelihood property of overfitting. Experimental results show that MRMRG algorithm has much better accuracy than most of existing BN learning algorithms when learning BNs from limited datasets.