期刊
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
卷 64, 期 7, 页码 1060-1070出版社
TAYLOR & FRANCIS LTD
DOI: 10.1057/jors.2012.120
关键词
credit scoring; class imbalance; resampling; logistic regression; support vector machine
资金
- Spanish Ministry of Education and Science [TIN2009-14205]
- Generalitat Valenciana [PROMETEO/2010/028]
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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