Resumen de Grey wolf optimization based parameter selection for support vector machines

Sathish Eswaramoorthy, N. Sivakumaran, Sankaranarayanan Sekaran

• Purpose – The purpose of this paper is to tune support vector machine (SVM) classifier using grey wolf optimizer (GWO).

  Design/methodology/approach – The schema of the work aims at extracting the features from the collected data followed by a SVM classifier and metaheuristic optimization to tune the classifier parameters.

  Findings – The optimal tuning of classifier parameters lowers errors due to manual elucidation and decreases the risk in human perceptions and repeated visual dignosis.

  Originality/value – A novel, GWO based tuning algorithm is used for SVM classifier, which is implemented in classifying the complex and nonlinear biomedical signals like intracranial electroencephalogram