A Comparision of Multiclass Support Vector Machine Algorithms

Author

Hao, Zhi-Feng ; Liu, Bo ; Yang, Xiao-Wei

Author_Institution

Coll. of Comput. Sci. & Eng., South China Univ. of Technol., Guangzhou

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

4221

Lastpage

4226

Abstract

Support vector machines (SVMS) were originally designed for binary classifications. For multi-classifications, they are usually converted into binary ones, and up to date, several methods have been proposed to decompose and reconstruct multi-class classification problems. In this paper, we compare the performance of these algorithms. They are applied to eight UCI data sets and the ten-folder method is adopted in our experiments. The results show that the one-against-one scheme is not always better than one-against-all scheme and one-against-all perform just as well as one-against-one approaches

Keywords

pattern classification; support vector machines; binary classification; multiclass classification problem; multiclass support vector machine algorithm; one-against-all scheme; one-against-one scheme; ten-folder method; Algorithm design and analysis; Computer science; Cybernetics; Design engineering; Educational institutions; Electronic mail; Laboratories; Machine learning; Machine learning algorithms; Mobile communication; Support vector machine classification; Support vector machines; Multi-Classification; One-against-All; Support Vector Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258947

Filename

4028813