NDLI: A binary-encoded tabu-list genetic algorithm for fast support vector regression hyper-parameters tuning

Content Provider	IEEE Xplore Digital Library
Author	Gascon-Moreno, J. Salcedo-Sanz, S. Ortiz-Garcia, E.G. Carro-Calvo, L. Saavedra-Moreno, B. Portilla-Figueras, J.A.
Copyright Year	2011
Description	Author affiliation: Department of Signal Theory and Communications Universidad de Alcalá, Madrid, Spain (Gascon-Moreno, J.; Salcedo-Sanz, S.; Ortiz-Garcia, E.G.; Carro-Calvo, L.; Saavedra-Moreno, B.; Portilla-Figueras, J.A.)
Abstract	The selection of hyper-parameters in support vector machines for regression (SVMr) is an essential step in the training process of these learning machines. Unfortunately, there is not an exact method to obtain the optimal values of SVM hyper-parameters. Therefore, it is necessary to use a search algorithm in order to find the best set of hyper-parameters. Grid Search is the most commonly used option to perform such a hyper-parameters search, though other possibilities based on evolutionary computation algorithms have been proposed in the literature. In this paper we analyze the use of a standard genetic algorithm with binary encoding, which allows a fast exploration of the hyper-parameters space. We include a kind of tabu-list in the proposed algorithm, where we keep the last individuals generated by the genetic algorithm to avoid re-training of the SVMr with them. This technique allows a good improvement of the SVMr training time respect to the grid search approach, while keeping the machine accuracy almost unaltered.
Starting Page	1253
Ending Page	1257
File Size	188282
Page Count	5
File Format	PDF
ISBN	9781457716768
ISSN	21647151
e-ISBN	9781457716768
DOI	10.1109/ISDA.2011.6121831
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines tabu-list Support Vector regression hyper-parameters estimation Evolutionary computation Encoding genetic algorithms Kernel Optimization Genetic algorithms
Content Type	Text
Resource Type	Article

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