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.
Bibliographical notePaper presented at the 2006 IEEE Congress on Evolutionary Computation (CEC 2006), 16-21 July 2006, Vancouver, Canada.
ISBN of the source publication: 9780780394872
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). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CEC.2006.1688409