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1、Chapter1C4.5NarenRamakrishnanContents1.1Introduction..............................................................11.2AlgorithmDescription....................................................31.3C4.5Features.............................................................71.3.1TreePr
2、uning......................................................71.3.2ImprovedUseofContinuousAttributes............................81.3.3HandlingMissingValues..........................................91.3.4InducingRulesets................................................101.4Discussio
3、nonAvailableSoftwareImplementations......................101.5TwoIllustrativeExamples...............................................111.5.1GolfDataset.....................................................111.5.2SoybeanDataset.................................................121.6A
4、dvancedTopics........................................................131.6.1MiningfromSecondaryStorage..................................131.6.2ObliqueDecisionTrees..........................................131.6.3FeatureSelection................................................131
5、.6.4EnsembleMethods..............................................141.6.5Classi?cationRules..............................................141.6.6Redescriptions...................................................151.7Exercises.........................................................
6、......15References...................................................................171.1IntroductionC4.5[30]isasuiteofalgorithmsforclassi?cationproblemsinmachinelearninganddatamining.Itistargetedatsupervisedlearning:Givenanattribute-valueddatasetwhereinstancesaredescribedbycol
7、lectionsofattributesandbelongtooneofasetofmutuallyexclusiveclasses,C4.5learnsamappingfromattributevaluestoclassesthatcanbeappliedtoclassifynew,unseeninstances.Forinstance,seeFigure1.1whererowsdenotespeci?cdays,attributesdenoteweatherconditionsonthegivenday,andtheclassdenoteswhet
8、hertheconditionsareconducivetoplayinggolf.Thus,
