Causal constraint pruning for exact learning of Bayesian network structure

TAN Xiangyuan; GAO Xiaoguang; HE Chuchao; WANG Zidong

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Causal constraint pruning for exact learning of Bayesian network structure

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How to improve the efficiency of exact learning of the Bayesian network structure is a challenging issue.In this paper,four different causal constraints algorithms are added into score calculations to prune possible parent sets,improving state-ofthe-art learning algorithms’efficiency.Experimental results indicate that exact learning algorithms can significantly improve the efficiency with only a slight loss of accuracy.Under causal constraints,these exact learning algorithms can prune about 70%possible parent sets and reduce about 60%running time while only losing no more than 2%accuracy on average.Additionally,with sufficient samples,exact learning algorithms with causal constraints can also obtain the optimal network.In general,adding max-min parents and children constraints has better results in terms of efficiency and accuracy among these four causal constraints algorithms.

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《系统工程与电子技术：英文版》 |2021年第4期|854-872|共19页
作者
TAN Xiangyuan; GAO Xiaoguang; HE Chuchao; WANG Zidong;
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作者单位

School of Electronics and Information;

Northwestern Polytechnical University;

Xi’an 710129;

China;

School of Electronics and Information Engineering;

Xi’an Technological University;

Xi’an 710021;

China;

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原文格式 PDF
正文语种 chi
中图分类英语;
关键词
Bayesian network; structure learning; exact learning algorithm; causal constraint;

Causal constraint pruning for exact learning of Bayesian network structure

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