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改进的基于DIV迭代查找和信息增益的网页特征选择算法综述报告.docx

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改进的基于DIV迭代查找和信息增益的网页特征选择算法综述报告 Title:ReviewReportonImprovedDIV-basedIterativeSearchandInformationGainWebFeatureSelectionAlgorithm Abstract: Webfeatureselectionisacrucialstepinwebpageclassificationandminingtasks.ThisreportprovidesacomprehensiveoverviewoftheimprovedDIV-basedIterativeSearchandInformationGain(IG)WebFeatureSelectionAlgorithm.Thealgorithmutilizesacombinationoftwoestablishedtechniquestoselectrelevantfeatures,namelyDIV-basedIterativeSearchandInformationGain.Thereportoutlinesthealgorithm'sprocess,advantages,andlimitations,andalsoexplorespotentialfutureimprovements. 1.Introduction: Inmoderntimes,thewealthofinformationavailableonthewebmakeseffectivefeatureselectionvitalforwebpageclassificationandminingtasks.TheDIV-basedIterativeSearchandInformationGainalgorithmisawell-establishedtechniqueusedtoselectrelevantfeaturesfromwebpages.Thisalgorithmofferstheadvantageofeffectivelyreducingthedimensionalityofthefeaturespace,enhancingtheaccuracyandefficiencyofsubsequentclassificationmodels. 2.DIV-basedIterativeSearch: TheDIV-basedIterativeSearchtechniqueisoneofthekeycomponentsofthealgorithm.Itspecificallyaddressestheproblemofirrelevantfeaturesbyiterativelyeliminatingthefeaturesthatbringminimaldiscriminationabilitytotheclassificationtask.Bymeasuringthefeature'sIndividualDiscriminativeValue(DIV),thealgorithmeffectivelyfiltersoutirrelevantfeatures.Thisstephelpsinreducingthenumberoffeaturesandimprovingtheperformanceofsubsequentclassificationtasks. 3.InformationGain: AnothercriticalcomponentofthealgorithmistheutilizationofInformationGain(IG)asameasureoftherelevanceoftheremainingfeatures.InformationGainhelpstoidentifyfeaturesthatcarryvaluableinformationregardingthetargetclassification.Bycalculatingtheentropyofthetargetvariablebeforeandafterconsideringafeature,thealgorithmassignsascoretoeachfeature,reflectingitsimportance.Thisstepfurtherrefinesthefeatureselectionprocess,ensuringthatonlythemostinformativefeaturesareretained. 4.ImprovedAlgorithm: TheimprovedDIV-basedIterativeSearc