An algorithm for cooperative learning of bayesian network structure from data

Authors:
Jiejun Huang;Heping Pan;Youchuan Wan
Affiliations:
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, P.R. China;School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, P.R. China;School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, P.R. China
Venue:
CSCWD'04 Proceedings of the 8th international conference on Computer Supported Cooperative Work in Design I
Year:
2004

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Abstract

Bayesian network is an important and powerful method for representing and reasoning under conditions of uncertainty, and has been widely used in artificial intelligence and knowledge engineering. Structure learning is certainly the most difficult problem in Bayesian network research. In this paper we give an introduction to Bayesian networks, and review the related work on leaning Bayesian networks. Then we discuss the major difficulties in structure learning, and propose an efficient algorithm for cooperative learning of Bayesian network structure from database. The experimental results from a case study prove that such an approach is feasible and robust.