Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm

Man Leung Wong, Yuan Yuan Guo
2008 Decision Support Systems  
This paper proposes a novel method for learning Bayesian networks from incomplete databases in the presence of missing values, which combines an evolutionary algorithm with the traditional Expectation Maximization (EM) algorithm. A data completing procedure is presented for learning and evaluating the candidate networks. Moreover, a strategy is introduced to obtain better initial networks to facilitate the method. The new method can also overcome the problem of getting stuck in sub-optimal
more » ... ions which occurs in most existing learning algorithms. The experimental results on the databases generated from several benchmark networks illustrate that the new method has better performance than some state-of-the-art algorithms. We also apply the method to a data mining problem and compare the performance of the discovered Bayesian networks with the models generated by other learning algorithms. The results demonstrate that our method outperforms other algorithms.
doi:10.1016/j.dss.2008.01.002 fatcat:jk5wsgyonndytjeoiqofxbssw4