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论文类型：会议论文

发表时间：2012-05-29

收录刊物：EI、Scopus

页面范围：666-670

摘要：Bayesian networks are efficient classification techniques, and widely applied in many fields, however, their structure learning is NP-hard. In this paper, a Bayesian network structure learning method called Tree-like Bayesian network (BN-TL) was proposed, which constructs the network by estimating the correlation between the features and the correlation between the class label and the features. Two metabolomics datasets about liver disease and five public datasets from the University of California at Irvine repository (UCI) were used to demonstrate the performance of BN-TL. The result shows that BN-TL outperforms the other three classifiers, including Na?ve Bayesian classifier (NB), Bayesian network classifier whose structure is learned by using K2 greedy search strategy (BN-K2) and a method proposed by Kuschner in 2010 (BN-BMC) in most cases. ? 2012 IEEE.