A Novel Greedy Bayesian Network Structure Learning Algorithm for Limited Data

Authors:
Feng Liu;Fengzhan Tian;Qiliang Zhu
Affiliations:
Department of Computer Science, Beijing University of Posts and Telecommunications, Xitu Cheng Lu 10, 100876 Beijing, China;Department of Computer Science, Beijing Jiaotong University, Shangyuan Cun 3, 100044 Beijing, China;Department of Computer Science, Beijing University of Posts and Telecommunications, Xitu Cheng Lu 10, 100876 Beijing, China
Venue:
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
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 MRMRG, Max Relevance and Min Redundancy Greedy algorithm. MRMRG algorithm is a variant of K2 algorithm for learning BNs from limited datasets. 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 efficiency and accuracy than most of existing BN learning algorithms when learning BNs from limited datasets.