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.
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