Causal constraint pruning for exact learning of Bayesian network structure  

作　　者：TAN Xiangyuan GAO Xiaoguang HE Chuchao WANG Zidong 

机构地区：[1]School of Electronics and Information,Northwestern Polytechnical University,Xi’an 710129,China [2]School of Electronics and Information Engineering,Xi’an Technological University,Xi’an 710021,China

出　　处：《Journal of Systems Engineering and Electronics》2021年第4期854-872,共19页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(61573285).

摘　　要：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.

关 键 词：Bayesian network structure learning exact learning algorithm causal constraint 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]