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Cited by 6,838

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

A comparison of methods for multiclass support vector machines

Chih-Wei Hsu, Chih-Jen Lin·2002·IEEE Transactions on Neural Networks
DOI 10.1109/72.991427
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Abstract

Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.

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Metadata

Authors
Hsu, C.-W., & Lin, C.-J.
Venue
IEEE Transactions on Neural Networks
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2002-03
Volume
13
Issue
2
Pages
415-425
ISSN
1045-9227 (ISSN-L)
DOI
10.1109/72.991427
OpenAlex
W1574496672
Cited by
6,838
References
2
FWCI
98.36
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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