Support vector machine learning with an evolutionary engine

R Stoean (), M Preuss, C Stoean (), E El-Darzi and D Dumitrescu
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R Stoean: University of Craiova
M Preuss: University of Dortmund
C Stoean: University of Craiova
E El-Darzi: University of Westminster
D Dumitrescu: University of Cluj-Napoca

Journal of the Operational Research Society, 2009, vol. 60, issue 8, 1116-1122

Abstract: Abstract The paper presents a novel evolutionary technique constructed as an alternative of the standard support vector machines architecture. The approach adopts the learning strategy of the latter but aims to simplify and generalize its training, by offering a transparent substitute to the initial black-box. Contrary to the canonical technique, the evolutionary approach can at all times explicitly acquire the coefficients of the decision function, without any further constraints. Moreover, in order to converge, the evolutionary method does not require the positive (semi-)definition properties for kernels within nonlinear learning. Several potential structures, enhancements and additions are proposed, tested and confirmed using available benchmarking test problems. Computational results show the validity of the new approach in terms of runtime, prediction accuracy and flexibility.

Keywords: evolutionary algorithms; support vector machines; classification; regression (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (1)

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DOI: 10.1057/jors.2008.124

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