In defense of one-vs-all classification. (English) Zbl 1222.68287

Rifkin, Ryan; Klautau, Aldebaro

J. Mach. Learn. Res. 5, 101-141 (2004).

Summary: We consider the problem of multiclass classification. Our main thesis is that a simple “one-vs-all” scheme is as accurate as any other approach, assuming that the underlying binary classifiers are well-tuned regularized classifiers such as support vector machines. This thesis is interesting in that it disagrees with a large body of recent published work on multiclass classification. We support our position by means of a critical review of the existing literature, a substantial collection of carefully controlled experimental work, and theoretical arguments.

MSC:

68T05	Learning and adaptive systems in artificial intelligence
62H30	Classification and discrimination; cluster analysis (statistical aspects)
68T10	Pattern recognition, speech recognition

Keywords:

multiclass classification; regularization