6533b85cfe1ef96bd12bc81d

RESEARCH PRODUCT

Optimization of Complex SVM Kernels Using a Hybrid Algorithm Based on Wasp Behaviour

Florin StoicaCorina SimianDana Simian

subject

Support vector machineData setOperator (computer programming)Polynomial kernelbusiness.industryComputer scienceKernel (statistics)Genetic algorithmEvolutionary algorithmPattern recognitionArtificial intelligencebusinessHybrid algorithm

description

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−Mijn 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.

yearjournalcountryeditionlanguage
2010-01-01
https://doi.org/10.1007/978-3-642-12535-5_42