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On the consistency of multiclass classification methods

Tewari, Ambuj & Bartlett, Peter L. (2007) On the consistency of multiclass classification methods. Journal of Machine Learning Research, 8, pp. 1007-1025.

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Abstract

Binary classification is a well studied special case of the classification problem. Statistical properties of binary classifiers, such as consistency, have been investigated in a variety of settings. Binary classification methods can be generalized in many ways to handle multiple classes. It turns out that one can lose consistency in generalizing a binary classification method to deal with multiple classes. We study a rich family of multiclass methods and provide a necessary and sufficient condition for their consistency. We illustrate our approach by applying it to some multiclass methods proposed in the literature.

Impact and interest:

50 citations in Scopus
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27 citations in Web of Science®

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ID Code: 44000
Item Type: Journal Article
Additional Information: Fulltext freely available see link above
Additional URLs:
Keywords: multiclass classification, consistency, Bayes risk, OAVJ
ISSN: 1533-7928
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Australian and New Zealand Standard Research Classification > PSYCHOLOGY AND COGNITIVE SCIENCES (170000) > COGNITIVE SCIENCE (170200)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
Copyright Owner: Copyright 2007 Journal of Machine Learning Research
Deposited On: 18 Aug 2011 08:39
Last Modified: 01 Mar 2012 00:34

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