Untangling AdaBoost-based Cost-Sensitive Classification. Part I: Theoretical Perspective
This work addresses the lack of a unifying analysis for cost-sensitive AdaBoost methods, which is a problem for researchers in machine learning.
The paper revisits various cost-sensitive AdaBoost algorithms to analyze their properties and behavior, aiming to identify the best-performing one through theoretical analysis.
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.