预览加载中，请您耐心等待几秒...

一种基于多标记的局部离群点检测算法.docx

预览
1/3
2/3
3/3

在线预览结束，喜欢就下载吧，查找使用更方便

下载提示 文本预览

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

一种基于多标记的局部离群点检测算法 基于多标记的局部离群点检测算法 Abstract: Outlierdetectionisanimportanttaskindataminingandhasbeenextensivelystudiedinrecentyears.Traditionaloutlierdetectionmethodsmainlyfocusondetectingoutliersinasingle-labelsetting,whereeachdatapointisassociatedwithonlyoneclasslabel.However,inmanyreal-worldapplications,datainstancesareoftenassociatedwithmultiplelabels,whichintroducesadditionalcomplexitytotheoutlierdetectionproblem.Inthispaper,weproposeanovelmulti-labellocaloutlierdetectionalgorithmtoaddressthisproblem. 1.Introduction Outlierdetectionplaysacrucialroleinvariousfieldssuchasfrauddetection,intrusiondetection,andmedicaldiagnosis.Itaimstoidentifydatainstancesthatsignificantlydeviatefromthenormalbehaviororpatternswithinagivendataset.Traditionaloutlierdetectionmethodsassumethateachdatainstanceisassociatedwithonlyonelabel.However,inmanyreal-worldscenarios,datainstancesoftenhavemultiplelabelsduetotheinherentcomplexityanddiversityofthedata. Multi-labeloutlierdetectionposesnewchallengesandrequiresthedevelopmentofinnovativealgorithmsthatcaneffectivelyhandlethisscenario.Inthispaper,weproposeamulti-labellocaloutlierdetectionalgorithmthatleveragesthelocalcharacteristicsofdatainstancestoidentifyoutliers. 2.RelatedWork Inrecentyears,severalapproacheshavebeenproposedformulti-labeloutlierdetection.Chenetal.proposedamethodbasedonthek-nearestneighbor(k-NN)algorithm,whichcomputestheoutlierscorebasedonthedistancebetweenadatainstanceanditsknearestneighbors.AnotherapproachbyZhangetal.utilizestheconceptofsubspacetocapturethelocalstructureofdatainstances,andthenidentifiesoutliersbyevaluatingtheirdistancestothesubspaces.Althoughthesemethodshaveachievedpromisingresults,theyarenotspecificallydesignedforthemulti-labelscenarioandmaynotfullyexploitthelabelinformation. 3.ProposedAlgorithm Ourproposedalgorithm,namedMulti-LabelLocalOutlierDetection(MLLOD),aimstoaddressthechallengesinmulti-labeloutlierdetection.Firstly,weemployak-nearestneighboralgorithmtocapturethelocalstructureofdatainstances.Instea