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Published in:
2011 | OriginalPaper | Chapter
On the Use of Boosting Procedures to Predict the Risk of Default
Authors : Giovanna Menardi, Federico Tedeschi, Nicola Torelli
Published in: Classification and Multivariate Analysis for Complex Data Structures
Publisher: Springer Berlin Heidelberg
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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.