Cost-sensitive ensemble learning: a unifying framework

被引：0
作者：
George Petrides
Wouter Verbeke
机构：
[1] University of Bergen,
[2] Vrije Universiteit Brussel (VUB),undefined
来源：
Data Mining and Knowledge Discovery | 2022年 / 36卷
关键词：
Cost-sensitive learning; Class imbalance; Classification; Misclassification cost;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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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页码：1 / 28
页数：27
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