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1、2009EighthInternationalConferenceonMobileBusinessAComparativeStudyontheAssessmentofPersonalCreditinUncertainEnvironments1,211QiRuihuaYangdeliHuRunbo1.InstituteofSystemEngineering,SchoolofManagement,DalianUniversityofTechnology,Dalian116024,China2.ModernEducationTechnologyCenter,Dali
2、anUniversityofForeignLanguage,Dalian116044,Chinarhqi@dlufl.edu.cnAbstractusedinplaceofcreditcards.However,thereishigheruncertaintyintheassessmentofcreditriskintheInthispaper,weevaluatedandcontrastedseveralmobileenvironment:limitedbandwidth,slowandclassifiersforcreditscoringinthemobi
3、leoftendisconnectedtransmission,inconvenientinputenvironment.Takingintoaccountthefeaturesofinterface,andsoon.Besides,thelimitedpowersupply,mobileenvironment,theclassificationperformanceislowprocessingspeedandlimitedmemoryofmobileassessedbytheprecisionandtheareaunderRocdeviceisnotcon
4、ducivetodealwithmissingcurve.NaiveCredalClassifierisalsointroducedtoinformation.Theadaptabilitytouncertaintyshouldbedealwithincompletecreditdatasetsinthemobilefullytakenintoaccountwhenselectingclassificationenvironment.algorithmfortheapplicationsinthemobileenvironment.1.Introduction
5、Thispaperisorganizedasfollows:WebeginwithashortoverviewofthelimitationofseveralclassificationtechniquesinthemobileenvironmentinInrecentyears,creditriskassessmenthasreceivedSection2.InSection3,wegiveabriefintroductionofextensiveattentionfromtheacademiaandthefinance.naïveBayesianclass
6、ifier.DatasetsandexperimentalCreditassessmentisimportanttoreducefinancialrisksdesignarepresentedinSection4.Section5givestheforthecreditinstitutions.Itcanbeseenasempiricalresultsanddiscussionforarealcreditclassificationproblem.Theloanapplicantsarescoringdataset.Section6addressestheco
7、nclusionandclassifiedintothe"creditgood"users,whoarediscussespossiblefutureresearchareas.expectedtorepaytheloan,andthe"badcredit"users,whomaynotrepaytheloan,accordingtothenaturalattributesandthecharacteristicsofconsumerbehavior.2.CreditscoringinthemobileThe"badcredit"user’sapplicati
8、onswillbecloselyenv
