Misclassification cost-sensitive ensemble learning: A unifying framework

George Petrides; Wouter Verbeke

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arXiv · 2007.07361

Misclassification cost-sensitive ensemble learning: A unifying framework

George Petrides·Wouter Verbeke

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Abstract

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.

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George Petrides, Wouter Verbeke. 2020-07-14. Misclassification cost-sensitive ensemble learning: A unifying framework. https://arxiv.org/abs/2007.07361

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