Abstract
Ensemble is a recently emerged computing technique to provide promising decisions by a consensus of multiple classifiers. The benefit of classifier ensembles has been demonstrated in a vast number of studies in the scope of credit risk management. Yet the performance of different ensemble models was rarely compared when the costs of misclassification errors are asymmetric. In this paper, we concentrate on the performance of 6 ensemble techniques in the context of cost-sensitive credit scoring using 3 financial data sets. The ensemble models are built on the basis of a set of component classifiers derived from different subsets of instances or features by a single learning algorithm. The performance of classifiers is evaluated in terms of expected misclassification cost and compared by nonparametric significance test. The experimental results demonstrate that the functionality of ensembles for boosting the performance of individual classifiers is closely related to the underlying learning algorithms and the employed ensemble techniques.