%0 Journal Article
%T Revisiting AdaBoost for Cost-Sensitive Classification. Part II: Empirical Analysis
%A Iago Landesa-V¨¢zquez
%A Jos¨¦ Luis Alba-Castro
%J Computer Science
%D 2015
%I arXiv
%X 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.
%U http://arxiv.org/abs/1507.04126v1