Open Access
Translator Disclaimer
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

Abstract

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.

Citation

Download Citation

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. https://doi.org/10.1214/08-AOAS198

Information

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

JOURNAL ARTICLE
17 PAGES


SHARE
Vol.2 • No. 4 • December 2008
Back to Top