Untangling AdaBoost-based Cost-Sensitive Classification. Part II: Empirical Analysis
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.
Keywords
Cite
@article{arxiv.1507.04126,
title = {Untangling AdaBoost-based Cost-Sensitive Classification. Part II: Empirical Analysis},
author = {Iago Landesa-Vázquez and José Luis Alba-Castro},
journal= {arXiv preprint arXiv:1507.04126},
year = {2016}
}
Comments
Extended version of paper submitted to Pattern Recognition (Revised in July 2016)