Untangling AdaBoost-based Cost-Sensitive Classification. Part II: Empirical Analysis

TL;DR

This paper empirically evaluates various AdaBoost-based cost-sensitive classification algorithms, concluding that simple cost-sensitive weight initialization often outperforms more complex methods.

Contribution

It provides an empirical comparison of existing algorithms, confirming that simple weight initialization is often the most effective approach.

Findings

Simple weight initialization performs best in many scenarios.

Complex methods do not consistently outperform simple approaches.

Empirical results support the theoretical analysis from Part I.

Abstract

A lot of approaches, each following a different strategy, have been proposed in the literature to provide AdaBoost with cost-sensitive properties. In the first part of this series of two papers, we have presented these algorithms in a homogeneous notational framework, proposed a clustering scheme for them and performed a thorough theoretical analysis of those approaches with a fully theoretical foundation. The present paper, in order to complete our analysis, is focused on the empirical study of all the algorithms previously presented over a wide range of heterogeneous classification problems. The results of our experiments, confirming the theoretical conclusions, seem to reveal that the simplest approach, just based on cost-sensitive weight initialization, is the one showing the best and soundest results, despite having been recurrently overlooked in the literature.