Untangling AdaBoost-based Cost-Sensitive Classification. Part I: Theoretical Perspective

TL;DR

This paper provides a unifying theoretical analysis of various AdaBoost-based cost-sensitive algorithms, clarifying their differences and properties to identify the most effective approach.

Contribution

It offers a comprehensive theoretical perspective that compares and analyzes different cost-sensitive AdaBoost algorithms within a unified framework.

Findings

Clarifies the theoretical properties of cost-sensitive AdaBoost variants

Provides criteria to compare the effectiveness of different algorithms

Sets the stage for practical evaluation in Part II

Abstract

Boosting algorithms have been widely used to tackle a plethora of problems. In the last few years, a lot of approaches have been proposed to provide standard AdaBoost with cost-sensitive capabilities, each with a different focus. However, for the researcher, these algorithms shape a tangled set with diffuse differences and properties, lacking a unifying analysis to jointly compare, classify, evaluate and discuss those approaches on a common basis. In this series of two papers we aim to revisit the various proposals, both from theoretical (Part I) and practical (Part II) perspectives, in order to analyze their specific properties and behavior, with the final goal of identifying the algorithm providing the best and soundest results.