A Bayesian network structure learning method for optimizing ordering search operator

一种优化节点序搜索算子的BN结构学习方法

School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, China

Received: 23 June 2022

Abstract

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%.

摘要

节点序空间下的局部搜索算法是一种性能良好的贝叶斯网络结构学习方法, 在学习效率上具有极大的优势。然而, 现有的该类算法通常存在节点序优化不足、学习精度低等问题, 并容易停止在一个局部最优。为解决这些问题, 对节点序空间下的局部搜索算法进行了研究, 提出了一种新的通过优化节点序搜索算子来提高贝叶斯网络结构学习精度的IWINOBS算法。将迭代局部搜索算法与窗口算子相结合来搜索节点序空间中给定节点序的邻域, 减小了算法陷入局部最优值的概率, 从而获得质量更高的网络结构。实验结果表明: 与网络结构空间下的贝叶斯网络结构学习算法相比, 所提算法的学习效率提升了54.12%;与现有节点序空间下的贝叶斯网络结构学习算法相比, 所提算法的学习精度提高了2.33%。