Grey wolf optimization based parameter selection for support vector machines

Sathish Eswaramoorthy ^[1] ; N. Sivakumaran ^[1] ; Sankaranarayanan Sekaran ^[1]
1. [1] National Institute Of Technology
  
  National Institute Of Technology
  
  Japón
Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 35, Nº 5, 2016, págs. 1513-1523
Idioma: inglés
Enlaces
- Texto completo
Resumen
- 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