Cost-sensitive ensemble learning: a unifying framework
Journal article, Peer reviewed
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OriginalversjonData mining and knowledge discovery. 2022, 36, 1-28. 10.1007/s10618-021-00790-4
Over the years, a plethora of cost-sensitive methods have been proposed for learning on data when different types of misclassification errors incur different costs. Our contribution is a unifying framework that provides a comprehensive and insightful overview on cost-sensitive ensemble methods, pinpointing their differences and similarities via a fine-grained categorization. Our framework contains natural extensions and generalisations of ideas across methods, be it AdaBoost, Bagging or Random Forest, and as a result not only yields all methods known to date but also some not previously considered.