Cost-Sensitive Boosting
Cost-Sensitive Boosting
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DOI：
10.1109/tpami.2010.71
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发表时间：
2011-02-01
影响因子：
23.6
通讯作者：
Vasconcelos, Nuno
中科院分区：
文献类型：
--
作者：
Masnadi-Shirazi, Hamed;Vasconcelos, Nuno
提出了一种新的框架，成本敏感的提升算法的设计。该框架基于最优成本敏感学习的两个必要条件的识别：1）期望损失必须由最优成本敏感决策规则最小化，2）经验损失最小化必须强调目标成本敏感边界的邻域。结果表明，这些条件使推导成本敏感的损失，可以通过梯度下降最小化，在弱学习者的凸组合的函数空间，产生新的提升算法。所提出的框架适用于推导成本敏感的扩展AdaBoost，RealBoost和LogitBoost。实验证据，与合成问题，标准数据集，和计算机视觉问题的人脸和汽车检测，提出支持成本敏感的最优的新算法。它们的性能也比较了以前的各种成本敏感的提升建议，以及流行的组合大利润分类器和概率校准。对成本敏感的提升被证明始终优于所有其他方法。
A novel framework is proposed for the design of cost-sensitive boosting algorithms. The framework is based on the identification of two necessary conditions for optimal cost-sensitive learning that 1) expected losses must be minimized by optimal cost-sensitive decision rules and 2) empirical loss minimization must emphasize the neighborhood of the target cost-sensitive boundary. It is shown that these conditions enable the derivation of cost-sensitive losses that can be minimized by gradient descent, in the functional space of convex combinations of weak learners, to produce novel boosting algorithms. The proposed framework is applied to the derivation of cost-sensitive extensions of AdaBoost, RealBoost, and LogitBoost. Experimental evidence, with a synthetic problem, standard data sets, and the computer vision problems of face and car detection, is presented in support of the cost-sensitive optimality of the new algorithms. Their performance is also compared to those of various previous cost-sensitive boosting proposals, as well as the popular combination of large-margin classifiers and probability calibration. Cost-sensitive boosting is shown to consistently outperform all other methods.