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1、毕业论文（设计） 外文翻译外文原文ImprovedUseofContinuousAttributesinC4.5J.R.QuinlanBasserDepartmentofComputerScienceUniversityofSydney,SydneyAustralia2006AbstractAreportedweaknessofC4.5indomainswithcontinuousattributesisaddressedbymodifyingtheformationandevaluationoftestsoncontinuousattributes.AnM
2、DL-inspiredpenaltyisappliedtosuchtests,eliminatingsomeofthemfromconsiderationandalteringtherelativedesirabilityofalltests.Empiricaltrialsshowthatthemodificationsleadtosmallerdecisiontreeswithhigherpredictiveaccuracies.ResultsalsoconfirmthatanewversionofC4.5incorporatingthesechanges
3、issuperiortorecentapproachesthatuseglobaldiscriminationandthatconstructsmalltreeswithmulti-intervalsplits.1.IntroductionMostempiricallearningsystemsaregivenasetofpre-classifiedcases,eachdescribedbyavectorofattributevalues,andconstructfromthemamappingfromattributevaluestoclasses.The
4、attributesusedtodescribecasescanbegroupedintocontinuousattributes,whosevaluesarenumeric,anddiscreteattributeswithunorderednominalvalues.Forexample,thedescriptionofapersonmightincludeweightinkilograms,withavaluesuchas73.5,andcolorofeyeswhosevalueisoneof`brown',`blue',etc.C4.5(Quinla
5、n,1993)isonesuchsystemthatlearnsdecision-treeclassifiers.SeveralauthorshaverecentlynotedthatC4.5'sperformanceisweakerindomainswithapreponderanceofcontinuousattributesthanforlearningtasksthathavemainlydiscreteattributes.Forexample,Auer,Holte,andMaass(1995)describeT2,asystemthatsearc
6、hesforgoodtwo-leveldecisiontrees,andcomment:TheaccuracyofT2'streesrivaledorsurpassedC4.5'son8ofthe[15]datasets,includingallbutoneofthedatasetshavingonlycontinuousattributes."Discussingtheeffectofreplacingcontinuousattributesbydiscreteattributes,eachofwhosevaluescorrespondstoaninter
7、valofthecontinuousattribute,Dougherty,Kowhai,andSalami(1995)write:C4.5'sperformancewassignificantlyimprovedontwodatasets:::usingtheentropydiscriminationmethodanddidnotsignificantlydegradeonanydataset.::WeconjecturethattheC4.5inductionalgorithmisnottakingfulladvantageofpossibleloca
8、ldiscrimination."Thispaper
