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.
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.
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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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