  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.
