A novel hybrid evolutionary algorithm for learning Bayesian networks from incomplete data

Yuan Yuan GUO, Man Leung WONG, Zhi Hua CAI

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

3 Citations (Scopus)

Abstract

Existing Structural Expectation-Maximization (EM) algorithms for learning Bayesian networks from incomplete data usually adopt the greedy hill climbing search method, which may make the algorithms find sub-optimal solutions. In this paper, we present a new Structural EM algorithm which employs a hybrid evolutionary algorithm as the search method. The experimental results on the data sets generated from several benchmark networks illustrate that our algorithm outperforms some state-of-the-art learning algorithms.
Original languageEnglish
Title of host publicationProceedings of the 2006 IEEE Congress on Evolutionary Computation, CEC 2006
PublisherInstitute of Electrical and Electronics Engineers
Pages916-923
Number of pages8
DOIs
Publication statusPublished - 1 Jan 2006

Bibliographical note

Paper presented at the 2006 IEEE Congress on Evolutionary Computation (CEC 2006), 16-21 July 2006, Vancouver, Canada.
ISBN of the source publication: 9780780394872

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