  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.
