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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Optimization of Complex SVM Kernels Using a Hybrid Algorithm Based on Wasp Behaviour
verfasst von : Dana Simian, Florin Stoica, Corina Simian
Erschienen in: Large-Scale Scientific Computing
Verlag: Springer Berlin Heidelberg
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The aim of this paper is to present a new method for optimization of SVM multiple kernels. The kernel substitution can be used to define many other types of learning machines distinct from SVMs. We introduced a new hybrid method which uses in the first level an evolutionary algorithm based on wasp behaviour and on the co-mutation operator
LR
−
M
ijn
and in the second level a SVM algorithm which computes the quality of chromosomes. The most important details of our algorithms are presented. The testing and validation proves that multiple kernels obtained using our genetic approach are improving the classification accuracy up to 94.12% for the “leukemia” data set.