计算机工程与应用2019，Vol.55Issue(11)：147-152,6.DOI:10.3778/j.issn.1002-8331.1803-0034
基于评分函数的贝叶斯网络结构融合算法
Bayesian Network Structure Merging Algorithm Based on Scoring Function
摘要
Abstract
Inferring the causality among variables using Bayesian networks has been applied widely in the field of artificial intelligence. The algorithms for constraint-based of constructing Bayesian networks usually return the Markov equivalent class of the real network from observed data, which cannot infer causality effectively because of the existence of undirected edges. In order to improve the inference of Bayesian networks, a model merging strategy combining the Bayesian network score function and the ensemble learning is proposed to reduce the number of undirected edges by integrating multiple Bayesian networks. The experimental results show that it can reduce the number of undirected edges apparently by merging weighted network structures and improve the accuracy of the final network structure as well, which validates the effective-ness of the algorithm.关键词
贝叶斯网络/评分函数/模型融合/因果推断Key words
Bayesian networks/score function/model merging/causal inference分类
信息技术与安全科学引用本文复制引用
蔡青松,陈希厚..基于评分函数的贝叶斯网络结构融合算法[J].计算机工程与应用,2019,55(11):147-152,6.基金项目
北京市自然科学基金(No.4172013). （No.4172013）
