简介：Creditriskpredictionmodelsseektopredictqualityfactorssuchaswhetheranindividualwilldefault(badapplicant)onaloanornot(goodapplicant).Thiscanbetreatedasakindofmachinelearning(ML)problem.Recently,theuseofMLalgorithmshasproventobeofgreatpracticalvalueinsolvingavarietyofriskproblemsincludingcreditriskprediction.OneofthemostactiveareasofrecentresearchinMLhasbeentheuseofensemble(combining)classifiers.Researchindicatesthatensembleindividualclassifiersleadtoasignificantimprovementinclassificationperformancebyhavingthemvoteforthemostpopularclass.Thispaperexploresthepredictedbehaviouroffiveclassifiersfordifferenttypesofnoiseintermsofcreditriskpredictionaccuracy,andhowcouldsuchaccuracybeimprovedbyusingpairsofclassifierensembles.Benchmarkingresultsonfivecreditdatasetsandcomparisonwiththeperformanceofeachindividualclassifieronpredictiveaccuracyatvariousattributenoiselevelsarepresented.Theexperimentalevaluationshowsthattheensembleofclassifierstechniquehasthepotentialtoimprovepredictionaccuracy.