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.

我们提出了一种多类别的成本敏感分类算法 BAdaCost，它在 Boosting 框架中结合了一组成本敏感的多类弱学习器，以获得强分类规则。通过引入 CMEL（成本敏感的多类别指数损失）推导出该算法，从而统一了 AdaBoost、SAMME、成本敏感 AdaBoost 和 PIBoost 等各种分类算法的优化损失函数，最终构建了一个共同的理论框架。通过实验，我们证明 BAdaCost 与之前的多类别成本敏感方法相比，在性能上取得了显著的提升。同时，我们还评估了该算法在多视角人脸和车辆检测问题中在不对称多类别分类中的优势。