Misclassification cost-sensitive ensemble learning: A unifying framework
Machine Learning
2020-07-16 v1 Machine Learning
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.
Cite
@article{arxiv.2007.07361,
title = {Misclassification cost-sensitive ensemble learning: A unifying framework},
author = {George Petrides and Wouter Verbeke},
journal= {arXiv preprint arXiv:2007.07361},
year = {2020}
}