In Defense of One-Vs-All Classification

作者：Ryan Rifkin, Aldebaro Klautau · 年份：2004 · 被引用次数：1383 · 研究领域：Face and Expression Recognition、Imbalanced Data Classification Techniques、Machine Learning and Algorithms

Editor: John Shawe-Taylor 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.