Robust loss functions for boosting
Robust loss functions for boosting
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DOI：
10.1162/neco.2007.19.8.2183
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发表时间：
2007-08-01
影响因子：
2.9
通讯作者：
Murata, Noboru
中科院分区：
文献类型：
--
作者：
Kanamori, Takafumi;Takenouchi, Takashi;Murata, Noboru
Boosting 被称为损失函数的梯度下降算法。人们经常指出，典型的 boosting 算法 Adaboost 受异常值的影响很大。在这封信中，研究了鲁棒增强的损失函数。基于鲁棒统计的概念，我们提出了一种损失函数的变换，使增强算法对极端异常值具有鲁棒性。接下来，将损失函数的截断应用于描述决策边界附近错误标签发生情况的污染模型。数值实验表明，与其他损失函数相比，从污染模型导出的所提出的损失函数对于处理高噪声数据非常有用。
Boosting is known as a gradient descent algorithm over loss functions. It is often pointed out that the typical boosting algorithm, Adaboost, is highly affected by outliers. In this letter, loss functions for robust boosting are studied. Based on the concept of robust statistics, we propose a transformation of loss functions that makes boosting algorithms robust against extreme outliers. Next, the truncation of loss functions is applied to contamination models that describe the occurrence of mislabels near decision boundaries. Numerical experiments illustrate that the proposed loss functions derived from the contamination models are useful for handling highly noisy data in comparison with other loss functions.