title: 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.
authors.affiliation: School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455 USA.
authors.forename: Hui
authors.initials: H
authors.lastname: Zou
authors.affiliation: Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109 USA.
authors.forename: Ji
authors.initials: J
authors.lastname: Zhu
authors.affiliation: Department of Statistics, Stanford University Stanford, California 94305 USA.
authors.forename: Trevor
authors.initials: T
authors.lastname: Hastie
keywords: Boosting; Fisher-consistent losses; multicategory classification