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1、MachineLearning1:81-106,1986©1986KluwerAcademicPublishers,Boston-ManufacturedinTheNetherlandsInductionofDecisionTreesJ.R,QUINLAN(munnarilnswitgould.oz!quinlan@seismo,css.gov)CentreforAdvancedComputingSciences,NewSouthWalesInstituteofTechnology,Sydney2007,Australia(ReceivedAugust1,1985)Keywords:cla
2、ssification,induction,decisiontrees,informationtheory,knowledgeacquisition,expertsystemsAbstract.Thetechnologyforbuildingknowledge-basedsystemsbyinductiveinferencefromexampleshasbeendemonstratedsuccessfullyinseveralpracticalapplications.Thispapersummarizesanapproachtosynthesizingdecisiontreesthath
3、asbeenusedinavarietyofsystems,anditdescribesonesuchsystem,ID3,indetail.Resultsfromrecentstudiesshowwaysinwhichthemethodologycanbemodifiedtodealwithinformationthatisnoisyand/orincomplete.Areportedshortcomingofthebasicalgorithmisdiscussedandtwomeansofovercomingitarecompared.Thepaperconcludeswithillu
4、strationsofcurrentresearchdirections.1.IntroductionSinceartificialintelligencefirstachievedrecognitionasadisciplineinthemid1950's,machinelearninghasbeenacentralresearcharea.Tworeasonscanbegivenforthisprominence.Theabilitytolearnisahallmarkofintelligentbehavior,soanyattempttounderstandintelligencea
5、saphenomenonmustincludeanunderstandingoflearn-ing.Moreconcretely,learningprovidesapotentialmethodologyforbuildinghigh-performancesystems.Researchonlearningismadeupofdiversesubfields.Atoneextremethereareadaptivesystemsthatmonitortheirownperformanceandattempttoimproveitbyadjustinginternalparameters.
6、Thisapproach,characteristicofalargeproportionoftheearlylearningwork,producedself-improvingprogramsforplayinggames(Samuel,1967),balancingpoles(Michie,1982),solvingproblems(Quinlan,1969)andmanyotherdomains.Aquitedifferentapproachseeslearningastheacquisitionofstructuredknowledgeintheformofconcepts(Hu
7、nt,1962;Winston,1975),discriminationnets(FeigenbaumandSimon,1963),orproductionrules(Buchanan,1978).Thepracticalimportanceofmachinelearningofthislatterkindhasbeenunderlin-82J.R.QUINLANedbytheadventofknowledge-base
