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Kernel width selection for SVM classification : a meta-learning approach

journal contribution
posted on 06.12.2017, 00:00 by A B M Shawkat AliA B M Shawkat Ali, K Smith
The most critical component of kernel-based learning algorithms is the choice of an appropriate kernel and its optimal parameters. In this paper, we propose a rule-based meta-learning approach for automatic radial basis function (RBF) kernel and its parameter selection for Support Vector Machine (SVM) classification. First, the best parameter selection is considered on the basis of prior information of the data with the help of Maximum Likelihood (ML) method and Nelder-Mead (N-M) simplex method. Then, the new rule-based meta-learning approach is constructed and tested on different sizes of 112 datasets with binary class as well as multi-classclassification problems. We observe that our rule-based methodology provides significant improvement of computational time as well as accuracy in some specific cases.

History

Volume

1

Issue

4

Start Page

78

End Page

97

Number of Pages

20

ISSN

1548-3924

Location

USA

Publisher

Idea Group Inc

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Monash University;

Era Eligible

Yes

Journal

International journal of data warehousing and mining.