In Defense of One-vs-All Classification
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.
Cite
Text
Rifkin and Klautau. "In Defense of One-vs-All Classification." Journal of Machine Learning Research, 2004.Markdown
[Rifkin and Klautau. "In Defense of One-vs-All Classification." Journal of Machine Learning Research, 2004.](https://mlanthology.org/jmlr/2004/rifkin2004jmlr-defense/)BibTeX
@article{rifkin2004jmlr-defense,
title = {{In Defense of One-vs-All Classification}},
author = {Rifkin, Ryan and Klautau, Aldebaro},
journal = {Journal of Machine Learning Research},
year = {2004},
pages = {101-141},
volume = {5},
url = {https://mlanthology.org/jmlr/2004/rifkin2004jmlr-defense/}
}