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

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