Untangling AdaBoost-based Cost-Sensitive Classification. Part II: Empirical Analysis
This work addresses the problem of selecting effective cost-sensitive classification methods for practitioners, though it is incremental as it builds on existing algorithms and theoretical analysis.
The paper empirically analyzes various AdaBoost-based cost-sensitive classification methods across diverse problems, finding that the simplest approach using cost-sensitive weight initialization yields the best results, confirming prior theoretical conclusions.
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.