期刊
DATA MINING AND KNOWLEDGE DISCOVERY
卷 36, 期 1, 页码 1-28出版社
SPRINGER
DOI: 10.1007/s10618-021-00790-4
关键词
Cost-sensitive learning; Class imbalance; Classification; Misclassification cost
资金
- University of Bergen (Haukeland University Hospital)
The study introduces a unified framework for cost-sensitive ensemble methods, categorizing and comparing them, including extensions and generalizations for methods like AdaBoost, Bagging, and Random Forest.
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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