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1、InternationalJournalofEngineeringandAdvancedTechnology(IJEAT)ISSN:2249–8958,Volume-1,Issue-4,April2012ADecisionTreeAlgorithmforUncertainDataK.Anuradha,N.TulasiRadha,T.PavanKumarAbstract—Classificationisaclassicalprobleminmachinelearninganddatamining.Givenasetoftrainingdatatuples,eachhav
2、ingaclasslabelandbeingrepresentedbyafeaturevector,thetaskistoalgorithmicallybuildamodelthatpredictstheclasslabelofanunseentesttuplebasedonthetuple’sfeaturevector.Oneofthemostpopularclassificationmodelsisthedecisiontreemodel.Decisiontreesarepopularbecausetheyarepracticalandeasytounderstan
3、d.Rulescanalsobeextractedfromdecisiontreeseasily.Treelearningalgorithmscangeneratedecisiontreemodelsfromatrainingdataset.Whenworkingonuncertaindataorprobabilisticdata,thelearningandpredictionalgorithmsneedhandletheuncertaintycautiously,orelsethedecisiontreecouldbeunreliableandpredictionr
4、esultsmaybewrong.Thispaperpresentsanewdecisiontreealgorithmforhandlinguncertaindata.IndexTerms—Classification,Decisiontree,Prediction,Uncertaindata.I.INTRODUCTIONDecisiontreesarepowerfulandpopulartoolsforFigure1:Anexampleofasimpledecisiontreeclassificationandprediction[1].Theattractivene
5、ssofdecisiontreesisduetothefactthat,incontrasttoneuralAdecisiontreecanbeusedtoclassifyanexamplebynetworks,decisiontreesrepresentrules.Rulescanreadilybestartingattherootofthetreeandmovingthroughituntilaexpressedsothathumanscanunderstandthemorevenleafnode,whichprovidestheclassificationofth
6、einstance.directlyusedinadatabaseaccesslanguagelikeSQLsothatDecisiontreeinduction[4]isatypicalinductiveapproachrecordsfallingintoaparticularcategorymayberetrieved.Intolearnknowledgeonclassification.Thekeyrequirementstosomeapplications,theaccuracyofaclassificationordominingwithdecisiontre
7、esare:predictionistheonlythingthatmatters.Insuchsituationswedonotnecessarilycarehoworwhythemodelworks.InotheroAttribute-valuedescription:objectorcasesituations,theabilitytoexplainthereasonforadecision,ismustbeexpressibleintermsofafixedcrucial.Inmarketingonehasdescribethec