A meta-learning approach to automatic kernel selection for support vector machines

Ali, Shawkat and Smith-Miles, Kate 2006, A meta-learning approach to automatic kernel selection for support vector machines, Neurocomputing, vol. 70, no. 1-3, pp. 173-186.

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Title A meta-learning approach to automatic kernel selection for support vector machines
Author(s) Ali, Shawkat
Smith-Miles, Kate
Journal name Neurocomputing
Volume number 70
Issue number 1-3
Start page 173
End page 186
Publisher Elsevier BV
Place of publication Amsterdam, The Netherlands
Publication date 2006-12
ISSN 0925-2312
1872-8286
Keyword(s) support vector machine
kernels
automatic selection
classification
Summary Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods such as support vector machine (SVM). Automatic kernel selection is a key issue given the number of kernels available, and the current trial-and-error nature of selecting the best kernel for a given problem. This paper introduces a new method for automatic kernel selection, with empirical results based on classification. The empirical study has been conducted among five kernels with 112 different classification problems, using the popular kernel based statistical learning algorithm SVM. We evaluate the kernels’ performance in terms of accuracy measures. We then focus on answering the question: which kernel is best suited to which type of classification problem? Our meta-learning methodology involves measuring the problem characteristics using classical, distance and distribution-based statistical information. We then combine these measures with the empirical results to present a rule-based method to select the most appropriate kernel for a classification problem. The rules are generated by the decision tree algorithm C5.0 and are evaluated with 10 fold cross validation. All generated rules offer high accuracy ratings.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2006, Elsevier B.V.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30009050

Document type: Journal Article
Collection: School of Engineering and Information Technology
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