An Evolutionary Artificial Immune System for feature selection and parameters optimization of support vector machines for ERP assessment in a P300-based GKT

Optimizing a classifier is a subject of great interest in the research area. A lot of methods inspired of biological metaphors are proposed for this task. This paper present a new algorithm based on the natural immune metaphors which select a proper subset of features and optimal parameters of a support vector machines (SVM) classifier. The designed optimization method is validated for ERP assessment in a P300-based GKT (guilty knowledge test). The result experiment shows the effectiveness of the method.