Title

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

Document Type

Conference paper

Source Publication

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

Publication Date

1-1-2006

First Page

916

Last Page

923

Publisher

Institute of Electrical and Electronics Engineers

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.

DOI

10.1109/CEC.2006.1688409

Publisher Statement

Copyright © 2006 IEEE. Access to external full text or publisher's version may require subscription.

Additional Information

Paper presented at the 2006 IEEE Congress on Evolutionary Computation (CEC 2006), 16-21 July 2006, Vancouver, Canada.

ISBN of the source publication: 9780780394872

Full-text Version

Publisher’s Version

Language

English

Recommended Citation

Guo, Y.-Y., Wong, M.-L., & Cai, Z.-H. (2006). A novel hybrid evolutionary algorithm for learning Bayesian networks from incomplete data. In Proceedings of the 2006 IEEE Congress on Evolutionary Computation, CEC 2006 (pp. 916-923). Piscataway: Institute of Electrical and Electronics Engineers. doi: 10.1109/CEC.2006.1688409

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