Cost-Sensitive Boosting

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Cascade R-CNN: High Quality Object Detection and Instance Segmentation (2019, background): “rs that produce few close false positives. The starting premise is that a single detector can only be optimal for a single quality level. This is known in the cost-sensitive learning literature [12], [42], where the optimization of different points of the receiver operating characteristic (ROC) requ”
Cascade R-CNN: Delving Into High Quality Object Detection (2017, background): “This is known in the cost-sensitive learning literature [7, 26], where the optimization of different points of the receiver operating characteristic (ROC) requires different loss functions.”
Advance and Prospects of AdaBoost Algorithm (2013, unknown): “研究指出 AdaBoost 有着内在的非对称性 , 可以直接应用于非对称学习 [80] , 算法使用的改变样本分布的策略 , 能够很自然地与经典代价敏感学习方法以及不平衡数据集学习方法相结合 , 使得 AdaBoost 及其变种算法已发展成为实现非对称学习的一种重要方法 [81 − 84] .”