On the suitability of resampling techniques for the class imbalance problem in credit scoring
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Other documents of the author: Marqués Marzal, Ana Isabel; García, Vicente; Sánchez Garreta, Josep Salvador
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comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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Title
On the suitability of resampling techniques for the class imbalance problem in credit scoringDate
2013Publisher
Palgrave MacmillanISSN
0160-5682; 1476-9360Bibliographic citation
MARQUÉS, A. I.; GARCÍA, V.; SÁNCHEZ, J. S. On the suitability of resampling techniques for the class imbalance problem in credit scoring. Journal of the Operational Research Society, 2012, vol. 64, no 7, p. 1060-1070Type
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/acceptedVersionSubject
Abstract
In real-life credit scoring applications, the case in which the class of defaulters is under-represented in comparison with the class of non-defaulters is a very common situation, but it has still received little ... [+]
In real-life credit scoring applications, the case in which the class of defaulters is under-represented in comparison with the class of non-defaulters is a very common situation, but it has still received little attention. The present paper investigates the suitability and performance of several resampling techniques when applied in conjunction with statistical and artificial intelligence prediction models over five real-world credit data sets, which have artificially been modified to derive different imbalance ratios (proportion of defaulters and non-defaulters examples). Experimental results demonstrate that the use of resampling methods consistently improves the performance given by the original imbalanced data. Besides, it is also important to note that in general, over-sampling techniques perform better than any under-sampling approach. [-]
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Journal of the Operational Research Society (2012), vol. 64, no 7Rights
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