In Defense of One-Vs-All Classification

  • Authors:
  • Ryan Rifkin;Aldebaro Klautau

  • Affiliations:
  • -;-

  • Venue:
  • The Journal of Machine Learning Research
  • Year:
  • 2004

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Abstract

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.