Misclassification cost-sensitive ensemble learning: A unifying framework

TL;DR

This paper introduces a unifying framework for cost-sensitive ensemble learning, systematically categorizing existing methods and extending them to include new variants, thus providing a comprehensive overview of the field.

Contribution

It offers a comprehensive, unified framework that encompasses and extends existing cost-sensitive ensemble methods like AdaBoost, Bagging, and Random Forest.

Findings

Includes all known cost-sensitive ensemble methods

Provides natural extensions and generalizations of existing ideas

Identifies previously unconsidered methods

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.