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On the suitability of resampling techniques for the class imbalance problem in credit scoring
dc.contributor.author | Marqués Marzal, Ana Isabel | |
dc.contributor.author | García, Vicente | |
dc.contributor.author | Sánchez Garreta, Josep Salvador | |
dc.date.accessioned | 2014-06-04T07:08:34Z | |
dc.date.available | 2014-06-04T07:08:34Z | |
dc.date.issued | 2013 | |
dc.identifier.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. 1060-1070 | ca_CA |
dc.identifier.issn | 0160-5682 | |
dc.identifier.issn | 1476-9360 | |
dc.identifier.uri | http://hdl.handle.net/10234/94370 | |
dc.description.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 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. | ca_CA |
dc.description.sponsorShip | This work has partially been supported by the Spanish Ministry of Education and Science under grant TIN2009– 14205 and the Generalitat Valenciana under grant PROMETEO/2010/ 028. | ca_CA |
dc.format.extent | 11 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Palgrave Macmillan | ca_CA |
dc.relation.isPartOf | Journal of the Operational Research Society (2012), vol. 64, no 7 | ca_CA |
dc.rights | Copyright © 2014 Palgrave Macmillan | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | Credit scoring | ca_CA |
dc.subject | Class imbalance | ca_CA |
dc.subject | Resampling | ca_CA |
dc.subject | Logistic regression | ca_CA |
dc.subject | Support vector | ca_CA |
dc.title | On the suitability of resampling techniques for the class imbalance problem in credit scoring | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | http://dx.doi.org/10.1057/jors.2012.120 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | http://www.palgrave-journals.com/jors/journal/v64/n7/full/jors2012120a.html | ca_CA |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
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