Journal Article

On the suitability of resampling techniques for the class imbalance problem in credit scoring

Marqués, A I, García, V, Sánchez, J S

2013

Overview

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.

• Publisher: Taylor & Francis,Palgrave Macmillan,Palgrave Macmillan UK,Taylor & Francis Ltd
• Subject: Algorithms / Artificial intelligence / Business and Management / class imbalance / Classification / Credit / Credit risk / Credit scoring / Data mining / Datasets / Decision trees / Discriminant analysis / General Paper / General Papers / Logistic regression / Machine learning / Management / Methods / Modeling / Operations research / Operations Research/Decision Theory / Regression analysis / resampling / Sampling techniques / Studies / support vector machine / Support vector machines