International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
The nature of statistical learning theory
The nature of statistical learning theory
Pattern Recognition Letters
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Explicitly representing expected cost: an alternative to ROC representation
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Information Retrieval
Support Vector Data Description
Machine Learning
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Extreme re-balancing for SVMs: a case study
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Estimating the Support of a High-Dimensional Distribution
Neural Computation
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Focusing on non-respondents: Response modeling with novelty detectors
Expert Systems with Applications: An International Journal
The class imbalance problem: A systematic study
Intelligent Data Analysis
A novelty detection approach to classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Building credit scoring models using genetic programming
Expert Systems with Applications: An International Journal
The novelty detection approach for different degrees of class imbalance
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Evaluating misclassifications in imbalanced data
ECML'06 Proceedings of the 17th European conference on Machine Learning
Improving risk predictions by preprocessing imbalanced credit data
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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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 two-class 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.