Learning Dynamic Bayesian Networks Structure Based on Bayesian Optimization Algorithm

Authors:
Song Gao;Qinkun Xiao;Quan Pan;Qingguo Li
Affiliations:
School of Automatization, Northwestern Polytechnical University, 710072 Xi'an, China;School of Electronic Information Engineering, Xi'an Technological University, 710032 Xi'an, China;School of Automatization, Northwestern Polytechnical University, 710072 Xi'an, China;School of Engineering Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

An optimization algorithm for dynamic Bayesian networks (DBN) based on Bayesian optimization algorithm (BOA) is developed for learning and constructing the DBN structure. In this paper, we first introduce some basic theories and concepts of probability model evolutionary algorithm. Then we describe, the basic mode for constructing DBN diagram and the mechanism of DBN structure learning based on BOA. The DBN structure learning based on BOA consists of two parts. The first part is to obtain the structure and parameters of DBN in terms of a good solution, and the second part is to produce new groups according to the obtained DBN structure. In this paper, the DBN learning is achieved by genetics algorithm based on a greedy mechanism. The DBN inference is performed by a forward-simulation algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.