WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Web Intelligence and Agent Systems
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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This paper describes a novel data mining approach thatemploys evolutionary programming to discover knowledgerepresented in Bayesian networks. There are two differentapproaches to the network learning problem. The first oneuses dependency analysis, while the second one searchesgood network structures according to a metric. Unfortu-nately,both approaches have their own drawbacks. Thus,we propose a novel hybrid algorithm of the two approaches,which consists of two phases, namely, the Conditional Inde-pendence(CI) test and the search phases. A new opera-toris introduced to further enhance the search efficiency.We conduct a number of experiments and compare the hy-bridalgorithm with our previous algorithm, MDLEP [18],which uses EP for network learning. The empirical resultsillustrate that the new approach has better performance.We apply the approach to a data sets of direct marketingand compare the performance of the evolved Bayesian net-worksobtained by the new algorithm with the models gen-eratedby other methods. In the comparison, the inducedBayesian networks produced by the new algorithm outper-formthe other models.