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arXiv · 1507.04126

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

Iago Landesa-Vázquez·José Luis Alba-Castro
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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.

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Iago Landesa-Vázquez, José Luis Alba-Castro. 2015-07-15. Untangling AdaBoost-based Cost-Sensitive Classification. Part II: Empirical Analysis. https://arxiv.org/abs/1507.04126

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