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1、TheApplicationsanditsStrategiesofAdaBoostAlgorithmintheCreditRatingsonFeatureSelection:TakingIronandSteelSectorForExampleLIHui1,21SchoolofStatistics,RenminUniversityofChina,Beijing,PRC,100872;2SchoolofScienceandInformation,QingdaoAgriculturalUniversity,Q
2、ingdao,PRC,266109Abstract：Inthispaper,Adaboostalgorithmisappliedincreditratingsfirstly,andempiricalanalysisshowsthatAdaboostalgorithmonthebasisof18indexesselectedasregressionvariablesfitsthecreditratesof39listedironandsteelcompaniesofChinaverywell.Thedis
3、criminationerrorsare2.56%after10iterations,giveniterationsaddedthenitserrorscouldreachzeroandoutputclassificationresultsstably.Inadditions,theindeximportanceoutputsusedfromtwoaspectscanreselectandrefinetheninekeyindexesamongtheeighteenindexes.AfterusingA
4、daboostalgorithmtotestagain,wecanfindthatthenineindexesreduceddonotcutdowntheclassificationinformationofthemodelsandtheratingcorrectionsdonotslidedowneither.Keywords：creditratings；featureselection；AdaBoostalgorithms；ironandsteelsectorⅠ.IntroductionMostof
5、regressionmodels,whichreflectrealisticeconomyandsocietyevents,arerelatetofeatureselection,andthencreditratinginvolvesthem.Theessenceofcreditratingsistoprovideariskysymboltoinvestors,discoveringanddeliveringsomeinformationofcreditrisks.Therefore,creditrat
6、ingdescribescreditriskbycreditsymbols,whichcouldbeconsideredasakindofclassifications,andthatisakindofqualitativeregressions.Fromfeatureselectiononqualitativeregression,wecanfindqualitativeregressionisboundupwithfeatureselectionandgoodorbadofselectionwill
7、influentscorrectionandeffectsdirectly.Howevermodernfeatureselectionmethodsaredividedasthreeclasses:Filter,Wrapper,Embedded[1].Filterisakindofgeneralfeatureselectionmethod,anditusessomescoresonindexesimportancetoaccomplishdatapreprocessing.BeforeFilterisu
8、sedtomodel,ascorestatisticshouldbebuiltthenrankedtoallindexes.Atlast,someunimportantindexeswillbecancelledunderstandardpredetermined.SomecommonFilterincludesPearsoncorrelationcoefficient,T-test,chi-squaretestandInformation
