ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2018, Vol. 55 ›› Issue (8): 1717-1725.doi: 10.7544/issn1000-1239.2018.20180197

Special Issue: 2018数据挖掘前沿进展专题

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A Measurable Bayesian Network Structure Learning Method

Qi Xiaolong1,2, Gao Yang1, Wang Hao1, Song Bei1, Zhou Chunlei3,Zhang Youwei3   

  1. 1(Department of Computer Science and Technology, Nanjing University, Nanjing 210046);2(Department of Electronics and Information Engineering, Yili Normal University, Yining, Xinjiang 835000);3(Jiangsu Frontier Electric Technology Co. Ltd., Nanjing 211102)
  • Online:2018-08-01

Abstract: In this paper, a Bayesian network structure learning method via variable ordering based on mutual information (BNS\+{vo}-learning) is presented, which includes two components: the metric information matrix learning and the “lazy” heuristic strategy. The matrix of measurement information characterizes the degree of dependency among variables and implies the degree of strength comparison, which effectively solves the problem of misjudgment due to order of variables in the independence test process. Under the guidance of metric information matrix, the “lazy” heuristic strategy selectively adds variables to the condition set in order to effectively reduce high-order tests and reduce the number of tests. We theoretically prove the reliability of the new method and experimentally demonstrate that the new method searches significantly faster than other search processes. And BNS\+{vo}-learning is easily extended to small and sparse data sets without losing the quality of the learning structure.

Key words: Bayesian network structure, mutual information, conditional independence test, variable order, false positive node, false negative node

CLC Number: