Learning without Default: A Study of One-Class Classification and the Low-Default Portfolio Problem

Abstract

Abstract. This paper asks at what level of class imbalance one-class classifiers outperform two-class classifiers in credit scoring problems in which class imbalance, referred to as the low-default portfolio problem, is a serious issue. The question is answered by comparing the performance of a variety of one-class and two-class classifiers on a selection of credit scoring datasets as the class imbalance is manipulated. We also include random oversampling as this is one of the most common approaches to addressing class imbalance. This study analyses the suitability and performance of recognised two-class classifiers and one-class classifiers. Based on our study we conclude that the performance of the twoclass classifiers deteriorates proportionally to the level of class imbalance. The two-class classifiers outperform one-class classifiers with class imbalance levels down as far as 15% (i.e. the imbalance ratio of minority class to majority class is 15:85). The one-class classifiers, whose performance remains unvaried throughout, are preferred when the minority class constitutes approximately 2% or less of the data. Between an imbalance of 2% to 15% the results are not as conclusive. These results show that one-class classifiers could potentially be used as a solution to the low-default portfolio problem experienced in the credit scoring domain.

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

“…When comparing the results given by logR in Table 3 with those of SVM in Table 4, it seems that the logistic regression model consistently performs better than the SVM approach, independently of the imbalance ratio. This finding is in agreement with the conclusions drawn in some previous studies (Baesens et al 2003, Xiao et al 2006, Kennedy et al 2010.…”

“…In the credit scoring domain, research has mainly focused on analyzing the behavior of prediction models, showing that the performance on the minority class drops down significantly as the imbalance ratio increases , Kennedy et al 2010, Bhattacharyya et al 2011, Brown and Mues 2012. However, only a few works have been addressed to design solutions for imbalanced credit data sets.…”

“…In the same direction of combining classifiers, Florez-Lopez (2010) employed several cooperative strategies (simple and weighted voting) based on statistical models and computational intelligence techniques in combination with bootstrapping to handle the imbalance problem. Kennedy et al (2010) explored the suitability and performance of various oneclass classifiers for several imbalanced credit scoring problems with varying levels of imbalance. The experimental results suggest that the one-class classifiers perform especially well when the minority class constitutes 2% or less of the data, whereas the two-class classifiers are preferred when the minority class represents at least 15% of the data.…”

Improving Risk Predictions by Preprocessing Imbalanced Credit Data

“…In the credit scoring domain, research has mainly focused on analyzing the behavior of conventional prediction models, showing that the performance on the minority class drops down significantly as the imbalance ratio increases [2,10]. However, only a few works have been addressed to design solutions for imbalanced credit data sets.…”

“…FlorezLopez [6] employed several cooperative strategies (simple and weighted voting) based on statistical models and artificial intelligence techniques in combination with bootstrapping to handle the low-default portfolio problem. Kennedy et al [10] explored the suitability and performance of one-class classifiers for several imbalanced credit scoring problems with varying levels of imbalance. The experimental results suggest that the one-class classifiers perform especially well when the minority class constitutes 2% or less of the data, whereas the two-class classifiers are preferred when the minority class represents at least 15% of the data.…”