A Hybrid Data Mining Approach to Discover Bayesian Networks Using Evolutionary Programming

Man Leung WONG, Shing Yan LEE, Kwong Sak LEUNG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Research

Abstract

Given the explosive growth of data collected from current business environment, data mining can potentially discover new
knowledge to improve managerial decision making. We propose a novel data mining approach that employs evolutionary programming to discover knowledge represented in Bayesian networks and apply the approach to marketing data. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second approach searches good network structures according to a metric. Unfortunately, the two approaches both have their own drawbacks. Thus, we propose a novel hybrid of the two approaches. With this new idea, we endeavor to improve upon our previous work, MDLEP, which uses evolutionary programming for network learning. We also introduce a new operator to further enhance the search efficiency. We conduct a number of experiments and compare the hybrid approach with MDLEP. The empirical results illustrate that the approach improves over MDLEP
Original languageEnglish
Title of host publicationGECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
PublisherMorgan Kaufmann Publishers, Inc.
Pages214-222
ISBN (Print)1558608788
Publication statusPublished - Jul 2002
Event2002 Genetic and Evolutionary Computation Conference - United States, New York, United States
Duration: 9 Jul 200213 Jul 2002

Conference

Conference2002 Genetic and Evolutionary Computation Conference
CountryUnited States
CityNew York
Period9/07/0213/07/02

Bibliographical note

This research was partially supported by the RGC Earmarked Grant LU 3012/01E.

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