Using evolutionary programming and minimum description length principle for data mining of bayesian networks

Wong, Man LeungMan LeungWongLam, WaiWaiLamProf. LEUNG Kwong Sak2023-03-302023-03-301999IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, Vol. 21 (2), pp. 174 - 17801628828http://hdl.handle.net/20.500.11861/7668We have developed a new approach (MDLEP) to learning Bayesian network structures based on the Minimum Description Length (MDL) principle and Evolutionary Programming (EP). It employs a MDL metric which is founded on information theory and integrates a knowledge-guided genetic operator for the optimization in the search process. ©1999 IEEE.enBayesian ModelMinimum Description LengthEvolutionary ProgrammingLength PrincipleMinimum Description Length PrincipleInformation TheoryGenetic OperatorsBayesian Network StructureLearning AlgorithmsOriginal NetworkEarly GenerationDirected Acyclic GraphNumber of StatisticsOffspring BehaviorSimple MutationGenetic Algorithm ApproachUsing evolutionary programming and minimum description length principle for data mining of bayesian networksPeer Reviewed Journal Article10.1109/34.748825