An Incremental Approach to Learning Bayesian Networks Containing Hidden Variables
Huang Houkuan · Dianzi xuebao · 2005
An incremental approach to learning Bayesian networks based on genetic algorithm,namely ILBN,is put forward in this paper.ILBN introduces the EM algorithm and genetic algorithm into the incremental process of Bayesian network learning,calculates the expectation of the sufficient statistics with incomplete data using EM algorithm and evolves network structures using genetic algorithm,that could avoid getting into local maxima to some extent.Furthermore,by defining a new mutation operator and extending the traditional crossover operator,ILBN could incrementally learn and evolve Bayesian networks containing hidden variables.Finally,ILBN improves the incremental process by Friedman et al.The experimental results show that,in terms of storage cost,ILBN is comparable with the method by Friedman et al,while under the same experimental conditions,ILBN could learn more accurate networks than that of Friedman et al.The experimental results also verify the validity of ILBN in presence of incomplete data and hidden variables.