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

Yuan Yuan GUO; Man Leung WONG; Zhi Hua CAI

doi:10.1109/CEC.2006.1688409

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

Yuan Yuan GUO, Man Leung WONG, Zhi Hua CAI

Department of Computing and Decision Sciences

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-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 language	English
Title of host publication	Proceedings of the 2006 IEEE Congress on Evolutionary Computation, CEC 2006
Publisher	Institute of Electrical and Electronics Engineers
Pages	916-923
Number of pages	8
DOIs	https://doi.org/10.1109/CEC.2006.1688409
Publication status	Published - 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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10.1109/CEC.2006.1688409

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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.",

author = "GUO, {Yuan Yuan} and WONG, {Man Leung} and CAI, {Zhi Hua}",

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A novel hybrid evolutionary algorithm for learning Bayesian networks from incomplete data. / GUO, Yuan Yuan; WONG, Man Leung; CAI, Zhi Hua.
Proceedings of the 2006 IEEE Congress on Evolutionary Computation, CEC 2006. Institute of Electrical and Electronics Engineers, 2006. p. 916-923.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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N2 - 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.

AB - 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.

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BT - Proceedings of the 2006 IEEE Congress on Evolutionary Computation, CEC 2006

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

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