A New Bayesian Network Structure Learning Algorithm Mechanism Based on the Decomposability of Scoring Functions

Guoliang LI  Lining XING  Zhongshan ZHANG  Yingwu CHEN  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E100-A   No.7   pp.1541-1551
Publication Date: 2017/07/01
Online ISSN: 1745-1337
Type of Manuscript: PAPER
Category: Graphs and Networks
Keyword: 
Bayesian network,  structure learning,  mutual information,  the decomposability of score functions,  binary particle swarm optimization,  

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Summary: 
Bayesian networks are a powerful approach for representation and reasoning under conditions of uncertainty. Of the many good algorithms for learning Bayesian networks from data, the bio-inspired search algorithm is one of the most effective. In this paper, we propose a hybrid mutual information-modified binary particle swarm optimization (MI-MBPSO) algorithm. This technique first constructs a network based on MI to improve the quality of the initial population, and then uses the decomposability of the scoring function to modify the BPSO algorithm. Experimental results show that, the proposed hybrid algorithm outperforms various other state-of-the-art structure learning algorithms.