Misclassification cost-sensitive ensemble learning: A unifying framework

• URL: http://arxiv.org/abs/2007.07361v1
• Date: Tue, 14 Jul 2020 21:18:33 GMT
• Title: Misclassification cost-sensitive ensemble learning: A unifying framework
• Authors: George Petrides and Wouter Verbeke
• Abstract summary: Our contribution is a unifying framework that provides a comprehensive and insightful overview on cost-sensitive ensemble methods. Our framework contains natural extensions and generalisations of ideas across methods, be it AdaBoost, Bagging or Random Forest.
• Score: 7.90398448280017
• License: http://creativecommons.org/licenses/by/4.0/
• 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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