Misclassification cost-sensitive ensemble learning: A unifying framework
This work provides a comprehensive overview for researchers and practitioners dealing with imbalanced or cost-sensitive classification tasks, but it is incremental as it synthesizes rather than introduces a novel method.
The authors tackled the problem of misclassification cost-sensitive ensemble learning by proposing a unifying framework that categorizes and generalizes existing methods, yielding both known and new approaches.
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.