BAdaCost: Multi-class Boosting with Costs

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. (C) 2018 Elsevier Ltd. All rights reserved.