组特征选择算法与其应用分析

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1、Abst髓lctAbStraCtClaSsificationmodelsandfIeatureselectionmetllodsongeneexpressionmicroarraydatainbioinfomaticsarenowthefocusofattentioniIlmachineleaminganddatamillingcommulli吼The仇nhmatgenesarelikelytoco—regulatein缸lctioningmeaJlsthatf．eature铲oupsdoexistiIlmic

2、roarraydata，anddomaillexpertswouldliketoexploittllem．Therefore，t11isp印erstudiesthegroupfeatureselectionalgorithms，t巧ingtofindthef．eanlregroupsWhjchproVidemoreinsightsintotlleuIlderlyingdatarelationshipsfordomainexperts，andmeanWhjlereducethedimensionalit)，and

3、improVeclaLssificationaccu姐cy．Generally，伊oupfeatureselectionalgoritllmscanbediVidedintotwocategoriesaccordingtothewayof且ndingfIeaturegroups：theexplicitonesaIldtlleimplicitones．111anexplicitgroupf-e狐Ⅱ℃selectionalgoritlu：11，groupsarefbuIlduIldersomecriterion，a

4、Ildf．eaturesinthes锄e掣oupareaLlwaysKghlycorrelated．ARermegroupsareidentified，t11ef．e咖reselectionalgorimmispe怕medonr印resentatiVefea：tures丘omeachgrouptoobtainthefinalgroupoffeanlres．BycontraSt，theimplicit伊oupfea_tureselectionalgorithmsdonotfind铲oupsdirectly，wKl

5、e伊oupscanbeidentifiedbytheresultsmatmef．eatureselectionalgorithmproVides．Forthese撕osortsof掣oupf．ea．tlJreselectionalgorimms，t11emaillcontributionsoftllispaperare：1．Proposeafbat吡eclusteringbasedexplicitgroupf．eatureselectionalgorithm，FC-gRFE(FeatureClustemgbaS

6、edGroupSVM-RFE)．111isalgori廿Hnpe疵msf．e栅eclusteringontlletI-aillingsettofindt11efeature铲oups，andmenselectf．eaturesatthegroupleVelViaSVMRFE．Inaddition，SW-gRFE(S锄pleWeightingFC-gRFE)砌chbaSedonFC-gRFEwi也s锄pleweightingisproposed．T11is“goritllIIlfirstlycalculatess

7、锄pleweightsaccordingtos锄pleimportanceandmenperfomsFC-gRFEtogetgroupfeatureselectionresultsonmeweighteds锄pleset．ExperimentalresultsonmiCroarraydatashowthattheproposedalgoritllmcanfindthef．eanlre伊oupsofdata，谢thoutsc撕母ingtheclaSsificationaccuracy．2．Proposeanimp

8、licitgroupfeatureselectionalgoritlunwithmodifiedweights，CW二groupS(Coe伍cientWeightgroupfIeamreSelection)．111ealgori‰firstlycodeseachf．eatureonmeoriginalfea_turesetViaaSparsemodel，E1aSticNet，andt1