New multicategory boosting algorithms based on multicategory Fisher-consistent losses
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.
Cite
@article{arxiv.0901.3988,
title = {New multicategory boosting algorithms based on multicategory Fisher-consistent losses},
author = {Hui Zou and Ji Zhu and Trevor Hastie},
journal= {arXiv preprint arXiv:0901.3988},
year = {2009}
}
Comments
Published in at http://dx.doi.org/10.1214/08-AOAS198 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)