Grey wolf optimization based parameter selection for support vector machines. Issue 5 (5th September 2016)

• Record Type: Journal Article
• Authors: Eswaramoorthy, Sathish; Sivakumaran, N.; Sekaran, Sankaranarayanan
• Is Part Of: Compel. Volume 35:Issue 5(2016)
• Page Start: 1513
• Page End: 1523
• Publication Date: 2016-09-05
• Subjects: Optimization techniques -- Algorithms -- Optimization -- Signal processing; Electrical engineering -- Data Processing -- Periodicals; Electrical engineering -- Mathematics -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Data Processing -- Periodicals; Electronics -- Mathematics -- Periodicals; 621.3
• DOI: 10.1108/COMPEL-09-2015-0337
• Languages: English
• ISSNs: 0332-1649

Abstract

Abstract : 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.