A comparison of methods for multiclass support vector machines
Chih-Wei Hsu
Chih-Jen Lin
Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei;
This paper appears in: Neural Networks, IEEE Transactions on
Publication Date: Mar 2002
Volume: 13,
Issue: 2
On page(s): 415-425
ISSN: 1045-9227
References Cited: 28
CODEN: ITNNEP
INSPEC Accession Number: 7224559
Digital Object Identifier: 10.1109/72.991427
Current Version Published: 2002-08-07
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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