Improving multiclass pattern recognition by the combination of two strategies

Author

García-Pedrajas, Nicolás ; Ortiz-Boyer, Domingo

Author_Institution

Dept. of Comput. & Numerical Anal., Campus Univ. de Rabanales, Cordoba, Spain

Volume

28

Issue

6

fYear

2006

fDate

6/1/2006 12:00:00 AM

Firstpage

1001

Lastpage

1006

Abstract

We present a new method of multiclass classification based on the combination of one-vs-all method and a modification of one-vs-one method. This combination of one-vs-all and one-vs-one methods proposed enforces the strength of both methods. A study of the behavior of the two methods identifies some of the sources of their failure. The performance of a classifier can be improved if the two methods are combined in one, in such a way that the main sources of their failure are partially avoided.

Keywords

pattern classification; classifier performance; multiclass classification method; multiclass pattern recognition; one-vs-all method; one-vs-one method; Algorithm design and analysis; Error correction codes; Hamming distance; Neural networks; Pattern recognition; Support vector machine classification; Support vector machines; Voting; Multiclass; classification; neural networks; one-vs-all; one-vs-one; support vector machines.; Algorithms; Artificial Intelligence; Cluster Analysis; Information Storage and Retrieval; Pattern Recognition, Automated;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2006.123

Filename

1624363