Learning bayesian networks structures based on memory binary particle swarm optimization

Authors:
Xiao-Lin Li;Shuang-Cheng Wang;Xiang-Dong He
Affiliations:
National Laboratory for Novel Software Technology Nanjing University, Nanjing, China;Department of Information Science, Shanghai Lixin University of Commerce, Shanghai, China;Nanjing Research & Development Center ZTE Corporation, Nanjing, China
Venue:
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Year:
2006

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Abstract

This paper describes a new data mining algorithm to learn Bayesian networks structures based on memory binary particle swarm optimization method and the Minimum Description Length (MDL) principle. An memory binary particle swarm optimization (MBPSO) is proposed. A memory influence is added to a binary particle swarm optimization. The purpose of the added memory feature is to prevent and overcome premature convergence by providing particle specific alternate target points to be used at times instead of the best current position of the particle. In addition, our algorithm, like some previous work, does not need to have a complete variable ordering as input. The experimental results illustrate that our algorithm not only improves the quality of the solutions, but also reduces the time cost.