08/03/2015 Multiple classifier application to credit risk assessment
http://www.sciencedirect.com/science/article/pii/S0957417409008847 1/2
doi:10.1016/j.eswa.2009.10.018
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Expert Systems with ApplicationsVolume 37, Issue 4, April 2010, Pages 3326–3336
Multiple classifier application to credit risk assessmentBhekisipho Twala ,
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Bhekisipho TwalaCorrigendum to “Multiple classifier application to credit risk assessment”[Expert Systems with Applications 37 (4) (2010) 3326–3336]Expert Systems with Applications, Volume 38, Issue 6, June 2011, Page 7909
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AbstractCredit risk prediction models seek to predict quality factors such as whether an individual will default (badapplicant) on a loan or not (good applicant). This can be treated as a kind of machine learning (ML)problem. Recently, the use of ML algorithms has proven to be of great practical value in solving a variety ofrisk problems including credit risk prediction. One of the most active areas of recent research in ML hasbeen the use of ensemble (combining) classifiers. Research indicates that ensemble individual classifierslead to a significant improvement in classification performance by having them vote for the most popularclass. This paper explores the predicted behaviour of five classifiers for different types of noise in terms ofcredit risk prediction accuracy, and how such accuracy could be improved by using classifier ensembles.Benchmarking results on four credit datasets and comparison with the performance of each individualclassifier on predictive accuracy at various attribute noise levels are presented. The experimentalevaluation shows that the ensemble of classifiers technique has the potential to improve predictionaccuracy.
KeywordsMachine learning; Supervised learning; Statistical pattern recognition; Ensemble; Credit risk prediction;Noise
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08/03/2015 Multiple classifier application to credit risk assessment
http://www.sciencedirect.com/science/article/pii/S0957417409008847 2/2
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