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一种改进的KNN分类方法.docx

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一种改进的KNN分类方法 Introduction: KNN(K-NearestNeighbors)isapopularalgorithmforclassificationandregressionproblems.Itisanon-parametricmethodthatworksbycomparingthedistancebetweennewtestdatapointstoexistinglabeleddatapointsinthetrainingset,choosingtheK-nearestneighborsandassigningtheclassofthemajorityoftheseneighborsasthepredictedclassforthenewdatapoint.Althoughitisasimpleandeffectivealgorithm,ithassomedrawbackssuchassensitivitytonoise,imbalancedclassdistributions,andvariabledistancesbetweendatapoints.Therefore,thereisaneedforanimprovedversionoftheKNNalgorithm. TheProposedMethod: TheproposedmethodaimstoaddresstheaforementioneddrawbacksoftheKNNalgorithmbyincorporatingthreemainimprovements:DistanceWeighting,LocalizedKNNandfeatureselection. 1.DistanceWeighting: OneofthemainissueswiththeKNNalgorithmisthatitassignsequalweightstoallthenearestneighborswithoutconsideringtheirdistancefromthenewdatapoint.Therefore,thismethodincorporatesadistance-weightingmechanismthatassignshigherweightstothenearestneighborsandlowerweightstothefartherneighbors.Thedistance-weightingfunctioncanbedefinedasfollows: w(i)=1/d(x,xi)^p Wherew(i)istheweightoftheithneighbor,d(x,xi)istheEuclideandistancebetweenthenewdatapointxandtheithneighborxi,andpisauser-definedparameterthatcontrolstheinfluenceofdistanceontheweightcalculation. 2.LocalizedKNN: AnotherissuewiththeKNNalgorithmisthatittreatsthewholedatasetasasingleentityanddoesnottakeintoaccountthelocalvariationsinthedistributionsofthedata.Therefore,thismethodincorporatesalocalizedKNNmechanismthatdividesthedatasetintosmallersub-regionsandidentifiesthenearestneighborswithineachofthesesub-regions.ThelocalizedKNNfunctioncanbedefinedasfollows: KNN(x,Xi)={xi|xiεXiandd(x,xi)≤d(x,xj)forallxjεXi} WhereKNN(x,Xi)isthesetofnearestneighborsofthenewdatapointx,withinthesub-regionXi. 3.FeatureSelection: Lastly,thismethodincorporatesafeatureselectionmechanismwhichidentifiesthemostrelevantfeaturesforclassificationusingavarietyoffeatureselectiontechniquessuchasMutualInformation,R