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December 2008 New multicategory boosting algorithms based on multicategory Fisher-consistent losses
Hui Zou, Ji Zhu, Trevor Hastie
Ann. Appl. Stat. 2(4): 1290-1306 (December 2008). DOI: 10.1214/08-AOAS198


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.


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Hui Zou. Ji Zhu. Trevor Hastie. "New multicategory boosting algorithms based on multicategory Fisher-consistent losses." Ann. Appl. Stat. 2 (4) 1290 - 1306, December 2008.


Published: December 2008
First available in Project Euclid: 8 January 2009

zbMATH: 1158.62044
MathSciNet: MR2655660
Digital Object Identifier: 10.1214/08-AOAS198

Keywords: boosting , Fisher-consistent losses , multicategory classification

Rights: Copyright © 2008 Institute of Mathematical Statistics


Vol.2 • No. 4 • December 2008
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