  We present BAdaCost, a multi-class cost-sensitive classification algorithm.
It combines a set of cost-sensitive multi-class weak learners to obtain a
strong classification rule within the Boosting framework. To derive the
algorithm we introduce CMEL, a Cost-sensitive Multi-class Exponential Loss that
generalizes the losses optimized in various classification algorithms such as
AdaBoost, SAMME, Cost-sensitive AdaBoost and PIBoost. Hence unifying them under
a common theoretical framework. In the experiments performed we prove that
BAdaCost achieves significant gains in performance when compared to previous
multi-class cost-sensitive approaches. The advantages of the proposed algorithm
in asymmetric multi-class classification are also evaluated in practical
multi-view face and car detection problems.
