In Defense of One-Vs-All Classification
Journal of Machine Learning ResearchPublished 1 December 2004
RifkinRyan, KlautauAldebaro
Citations895
SJR quartileQ1
SJR score2.02
SNIP3.07
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
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 ...
Keywords
Computer Science