Abstract
In this paper, a new method, called EM-EA, is put forward for learning Bayesian network structures from incomplete data. This method combines the EM algorithm with an evolutionary algorithm (EA) and transforms the incomplete data to complete data using EM algorithm and then evolve network structures using the evolutionary algorithm with the complete data. In order to learn Bayesian networks with hidden variables, a new mutation operator has been introduced and the function of the crossover has been correspondingly expanded. The results of the experiments show that EM-EA is more accurate and practical than other network structure learning algorithms that deal with the incomplete data.KeywordsNetwork StructureEvolutionary AlgorithmBayesian NetworkIncomplete DataMutation OperatorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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