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

By Kenneth Kennedy, Brian Mac Namee and Sarah Jane Delany

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 i

Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.359.5609
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