NEW MULTICATEGORY BOOSTING ALGORITHMS BASED ON MULTICATEGORY FISHER-CONSISTENT LOSSES.
NEW MULTICATEGORY BOOSTING ALGORITHMS BASED ON MULTICATEGORY FISHER-CONSISTENT LOSSES.
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DOI：
10.1214/08-aoas198
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发表时间：
2008-12
期刊：
影响因子：
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通讯作者：
Hastie T
中科院分区：
文献类型：
--
作者：
Zou H;Zhu J;Hastie T
Fisher一致性损失函数在构造成功的基于边缘的二进制分类器中起着重要的作用。本文建立了多类分类问题的Fisher相容性条件。我们的方法使用的边缘向量的概念，可以被视为一个多类别的推广的二进制边缘。我们刻画了一类光滑的凸损失函数是Fisher一致的多类别分类。然后，我们考虑使用基于边缘向量的损失函数来推导多类别提升算法。特别是，我们通过使用指数和逻辑回归损失推导出两个新的多类别提升算法。
Fisher-consistent loss functions play a fundamental role in the construction of successful binary margin-based classifiers. In this paper we establish the Fisher-consistency condition for multicategory classification problems. Our approach uses the margin vector concept which can be regarded as a multicategory generalization of the binary margin. We characterize a wide class of smooth convex loss functions that are Fisher-consistent for multicategory classification. We then consider using the margin-vector-based loss functions to derive multicategory boosting algorithms. In particular, we derive two new multicategory boosting algorithms by using the exponential and logistic regression losses.