Precision of multi-class classification methods for support vector machines

A single support vector machines can only deal with binary-class classification. In the face of multi-class classification task, we usually combine multi-support vector machines together, strategies include one-against-one, one-against-the-rest and Binary Tree, etc. Previously precisions of these cl...

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Bibliographic Details
Published in2008 9th International Conference on Signal Processing pp. 1516 - 1519
Main Authors Honglian Li, Ruili Jiao, Jing Fan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2008
Subjects
Binary trees
Classification Precision
Classification tree analysis
Data engineering
Educational technology
Information science
Multi-class Classification
Pattern recognition
Support vector machine classification
Support vector machines
Testing
Voting
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Summary:A single support vector machines can only deal with binary-class classification. In the face of multi-class classification task, we usually combine multi-support vector machines together, strategies include one-against-one, one-against-the-rest and Binary Tree, etc. Previously precisions of these classification methods are drawn from experiments. In this paper we propose precision formulae of one-against-the-rest and Binary Tree classification methods, experiments show our conclusions are convincing.
ISBN:1424421780
9781424421787
ISSN:2164-5221
DOI:10.1109/ICOSP.2008.4697421