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基于深度学习与随机森林的高维数据特征选择.docx

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基于深度学习与随机森林的高维数据特征选择 I.Introduction Therapiddevelopmentoftechnologyhasledtothegenerationofmassivehigh-dimensionaldata,whichposessignificantchallengestodataanalysis.Featureselection,asanessentialstepindatapreprocessing,aimstoselectrelevantfeaturesandremoveredundantornoisyfeaturesfromtheoriginaldataset,whichcanimprovetheaccuracyandefficiencyofsubsequentanalysistasks.Inrecentyears,deeplearningandrandomforesthavebeenwidelyusedinfeatureselectionduetotheirpowerfulfeaturerepresentationandselectionabilities.Thispaperprovidesacomprehensiveoverviewofthehigh-dimensionaldatafeatureselectionmethodsbasedondeeplearningandrandomforest. II.FeatureSelection Featureselectionisaprocessofselectingasubsetofrelevantfeaturesfromtheoriginalfeaturespace,whichcanimprovetheaccuracyandefficiencyofsubsequentanalysistasks.Therearethreemaincategoriesoffeatureselectionmethods:filtermethods,wrappermethods,andembeddedmethods. Filtermethodsrankfeaturesbasedonacertaincriterion,suchascorrelationcoefficient,mutualinformation,orvariance.Wrappermethodsevaluatetheperformanceofasubsetoffeaturesbyusingamachinelearningalgorithmandsearchfortheoptimalfeaturesubsetthroughaheuristicsearchalgorithm.Embeddedmethodsincorporatefeatureselectionintothemachinelearningalgorithmandselectfeaturesduringthetrainingprocess. III.DeepLearning-basedFeatureSelection Deeplearning,withitspowerfulfeaturerepresentationability,hasbeenwidelyusedinvariousdataanalysistasks,includingfeatureselection.Thereareseveraldeeplearning-basedfeatureselectionmethods,includingautoencoder-basedfeatureselection,sparsecoding-basedfeatureselection,anddeepbeliefnetwork-basedfeatureselection. Autoencoder-basedfeatureselectionconstructsadeepneuralnetworkwithanencoderandadecoder.Theencodermapstheinputfeaturestoalow-dimensionallatentspace,andthedecodermapsthelatentfeaturesbacktotheoriginalfeaturespace.Theautoencoderistrainedtominimizethereconstructionerrorbetweentheoriginalfeaturesandthereconstructedfeatures.Thefeatureswithsmallreconstructionerrorsareselecteda