Statistical models have been widely applied with the aim of evaluating the risk of default of enterprises. However, a typical problem is that the occurrence of the default event is rare, and this class imbalance strongly affects the performance of traditional classifiers. Boosting is a general class of methods which iteratively enforces the accuracy of any weak learner, but it suffers from some drawbacks in presence of unbalanced classes. Performance of standard boosting procedures to deal with unbalanced classes is discussed and a new algorithm is proposed. © Springer-Verlag Berlin Heidelberg 2011.
CITATION STYLE
Menardi, G., Tedeschi, F., & Torelli, N. (2011). On the use of boosting procedures to predict the risk of default. In Studies in Classification, Data Analysis, and Knowledge Organization (pp. 211–218). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-642-13312-1_21
Mendeley helps you to discover research relevant for your work.