基于多元变量组合的回归支持向量机集成模型及其应用.docx

基于多元变量组合的回归支持向量机集成模型及其应用
Abstract:
RegressionSupportVectorMachine(RSVM)isapowerfultoolforpredictionandestimationinmanyfields.However,itmaynotalwaysworkwellwhenthedataincludesmultiplevariableswithcomplexinteractions.Inthispaper,weproposeanewapproach,calledMulti-VariableCombinationRegressionSupportVectorMachine(MCRSVM),whichintegratesmultipleregressionsupportvectormachinestomodeltheinteractionsbetweenvariables.Usingsimulationexperimentsandreal-worlddataanalysis,wedemonstratethatMCRSVMcanoutperformseveralexistingmethodsforpredictingtheresponsevariable.
Introduction:
RegressionSupportVectorMachine(RSVM)iswidelyusedforpredictionandestimationinvariousfieldssuchasengineering,finance,andbiology.RSVMaimstoidentifyahyperplanethatmaximizesthemarginbetweentwogroups.However,whenthedataincludesmultiplevariables,RSVMmaynotalwaysworkwellsinceitonlyconsiderslinearcombinationsofthevariables.
Toaddressthisproblem,weproposeanewapproach,calledMulti-VariableCombinationRegressionSupportVectorMachine(MCRSVM),whichintegratesmultipleregressionsupportvectormachinestomodeltheinteractionsbetweenvariables.MCRSVMtakesadvantageofthekerneltrickandthemultiplekernellearningalgorithmtocombinedifferentkernels.OurapproachcanmodelthecomplexinteractionsbetweenvariablesandimprovetheperformanceofRSVMforpredictionandestimation.
Methodology:
TheMCRSVMapproachconsistsofthreesteps:
First,weapplymultipleregressionsupportvectormachinestoeachvariabletoobtainthecoefficientvectorforthatvariable.Thecoefficientvectordescribestherelationshipbetweenthevariableandtheresponsevariable.
Second,wecombinethecoefficientvectorsforallvariablesusingmultiplekernellearningalgorithms.Themultiplekernellearningalgorithmusesaweightedcombinationofdifferentkernelstoobtainthebestpossiblekernelfunction.Bycombiningdifferentkernels,wecancapturethecomplexinteractionsbetweenvariables.
Third,weusethecombinedkernelfunctiontotrainasupportvectormachineforpredictionandestimation.Thetrainedmodelcanbeusedtopredicttheresponsevariablefornewdata.
Result