Data streams classification with ensemble model based on decision-feedback-中国期刊网

摘要 Themainchallengesofdatastreamsclassificationincludeinfinitelength,concept-drifting,arrivalofnovelclassesandlackoflabeledinstances.Mostexistingtechniquesaddressonlysomeofthemandignoreothers.Soanensembleclassificationmodelbasedondecision-feedback(ECM-BDF)ispresentedinthispapertoaddressallthesechallenges.Firstly,adatastreamisdividedintosequentialchunksandaclassificationmodelistrainedfromeachlabeleddatachunk.Toaddresstheinfinitelengthandconcept-driftingproblem,afixednumberofsuchmodelsconstituteanensemblemodelEandsubsequentlabeledchunksareusedtoupdateE.Todealwiththeappearanceofnovelclassesandlimitedlabeledinstancesproblem,themodelincorporatesanovelclassdetectionmechanismtodetectthearrivalofanovelclasswithouttrainingEwithlabeledinstancesofthatclass.Meanwhile,unsupervisedmodelsaretrainedfromunlabeledinstancestoprovideusefulconstraintsforE.AnextendedensemblemodelExcanbeacquiredwiththeconstraintsasfeedbackinformation,andthenunlabeledinstancescanbeclassifiedmoreaccuratelybysatisfyingthemaximumconsensusofEx.ExperimentalresultsdemonstratethattheproposedECM-BDFoutperformstraditionaltechniquesinclassifyingdatastreamswithlimitedlabeleddata.

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Data streams classification with ensemble model based on decision-feedback

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Data streams classification with ensemble model based on decision-feedback

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