%0 Journal Article
%A CAI Qingsong
%A CHEN Xihou
%T Bayesian Network Structure Merging Algorithm Based on Scoring Function
%D 2019
%R 10.3778/j.issn.1002-8331.1803-0034
%J Computer Engineering and Applications
%P 147-152
%V 55
%N 11
%X 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 effectiveness of the algorithm.
%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1803-0034