A Bayesian network structure learning method for optimizing ordering search operator

被引：1

作者：

Jia L. ^{[1
]}

Dong M. ^{[1
]}

He C. ^{[1
]}

Di R. ^{[1
]}

Li X. ^{[1
]}

机构：

[1] School of Electronic Information Engineering, Xi′an Technological University, Xi′an

来源：

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University | 2023年 / 41卷 / 02期

关键词：

Bayesian network; local search; order optimization; search operator; structure learning;

D O I：

10.1051/jnwpu/20234120419

中图分类号：

学科分类号：

摘要：

Local search algorithm in ordering space is a good method which can effectively improve the efficiency of bayesian network structure learning. However, the existing algorithms usually have problems such as insufficient order optimization, low learning accuracy, and easy stop at a local optimal. In order to solve these problems, the local search algorithm in ordering space is studied, and a new method to improve the accuracy of bayesian network structure learning by optimizing order search operator is proposed. Combining the iterative local search algorithm with the window operator to search the neighborhood of a given order in the ordering space, the probability of the algorithm falling into the local optimal value is reduced, and the network structure with higher quality is obtained. Experimental results show that comparing with the bayesian network structure learning algorithm in network structure space, the learning efficiency of the present algorithm is improved by 54.12%. Comparing with the bayesian network structure learning algorithm in ordering space, the learning accuracy of the present algorithm is improved by 2.33%. ©2023 Journal of Northwestern Polytechnical University.

引用

页码：419 / 427

页数：8

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