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ID:33937444
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页数:10页
时间:2019-03-02
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1、Feature-basedClassicationofTime-seriesDataALEXNANOPOULOSROBALCOCKYANNISMANOLOPOULOSDataEngineeringLab,DepartmentofInformaticsAristotleUniversityThessaloniki54006GREECEfalex,rob,manolopog@delab.csd.auth.grAbstract:Inthispaperweproposetheuseofstatisticalfeaturesfortime-seriesclassication.Theclas-si
2、cationisperformedwithamulti-layerperceptron(MLP)neuralnetwork.TheproposedmethodisexaminedinthecontextofControlChartPatterndata,whicharetimeseriesusedinStatisticalProcessControl.Experimentalresultsverifytheeciencyofthefeature-basedclassicationmethod,comparedtopreviousmethodswhichclassifytimeseries
3、basedonthevaluesofeachtimepoint.Moreover,theresultsshowtherobustnessoftheproposedmethodagainstnoiseandtime-serieslength.Key-words:Datamining,timeseries,classication,statisticalfeatures1IntroductionDataminingistheprocessofpatternidenticationinlargedatabases[1].Themainobjectivesofdataminingarepredic
4、tionanddescription.Dataminingmethodsbelongtoseveralcategories.Regressionmapsdatatopredictionvalues.Generalizationproducesasimpledescriptionfromcomplexdataandassociationndsdependenciesamongdata.Clusteringidentiesasetoftypeswithwhichdatacanbecategorized,whereasclassicationmapsdatatoasetofapredened
5、types.Datamininghasbeenmainlyappliedtorelationaldata.Non-relationaldatapresentimportantchallengestodataminingduetotheirsizeanddimensionality.Time-seriesdataaresupportedbymanydatabasesystems.Atimeseriesisasequenceofrealnumbersrepresentingthevaluesofavariableovertime.Theyhavefoundapplicationsintempora
6、l[2]andscienticdatabases,aswellasindatawarehousescontainingavarietyofdatatypes,fromstockmarketpricestoelectro-cardiograms.Miningtime-seriesdatacanrevealimportantpatterns,suchassimilarities[3],trends[4]orperiodicity[5].Sincetime-seriesdatatendtogrowrapidlyovertime,theypresentseveralperformanceissues
7、todataminingalgorithms.Time-seriesclassicationinvolvesthelearningofafunctionthatmapsaseriesintoaclassfromasetofpredenedclasses.Therefore,itrequirestheexistenceofasetofwell-denedcategories.Theclassd
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