Grey wolf optimization based parameter selection for support vector machines
S Eswaramoorthy, N Sivakumaran… - … International journal for …, 2016 - dialnet.unirioja.es
S Eswaramoorthy, N Sivakumaran, S Sekaran
Compel: International journal for computation and mathematics in …, 2016•dialnet.unirioja.esPurpose–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 …
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 …
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
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