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基于改进烟花算法的SVM特征选择和参数优化的研究.docx

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基于改进烟花算法的SVM特征选择和参数优化的研究 Title:ResearchonSVMFeatureSelectionandParameterOptimizationbasedonImprovedFireworksAlgorithm Abstract: SupportVectorMachine(SVM)featureselectionandparameteroptimizationaretwocrucialstepsinsolvingcomplexclassificationproblems.ThispaperproposesanovelapproachthatcombinestheImprovedFireworksAlgorithm(IFA)withSVMtoenhancefeatureselectionandoptimizeparameterssimultaneously.TheIFAisutilizedtoexplorethefeaturespaceandsearchfortheoptimalsubsetoffeatures,whilealsooptimizingtheSVMparameters.AcomparativeanalysisisperformedwithtraditionalSVM,demonstratingtheeffectivenessandefficiencyoftheproposedapproach.Experimentalresultsonseveralbenchmarkdatasetsvalidatethesuperiorityoftheproposedapproach,achievingimprovedclassificationaccuracyandreducedcomputationalcost. Keywords:SupportVectorMachines,FeatureSelection,ParameterOptimization,ImprovedFireworksAlgorithm,ClassificationAccuracy 1.Introduction Inmanyreal-worldapplications,suchasimagerecognition,bioinformatics,andtextmining,featureselectionplaysavitalroleinconstructingeffectiveclassificationmodels.Theselectionofrelevantfeaturesnotonlyimprovestheperformanceofclassificationalgorithmsbutalsoreducescomputationalcomplexity.Moreover,choosingsuitableparametersforSVMissignificantforachievingaccurateclassificationresults.However,thechallengeliesinfindingtheoptimalsubsetoffeaturesandadjustingtheSVMparametersconcurrently. 2.RelatedWork 2.1SupportVectorMachines SupportVectorMachines(SVM)isapopularmachinelearningalgorithmforbinaryclassification.SVMaimstofindanoptimalhyperplanethatseparatestheinputinstancesintodifferentclasses.TheeffectivenessofSVMsignificantlydependsontheselectionofrelevantfeaturesandappropriateparametervalues. 2.2FeatureSelectionMethods Severalfeatureselectionmethodshavebeenextensivelystudied,includingfiltermethods,wrappermethods,andembeddedmethods.Thesemethodsevaluatetherelevanceandimportanceoffeaturesbasedonvariouscriteria. 2.3FireworksAlgorithm TheFireworksAlgorithm(FWA)isapopulation-basedoptimizatio