Support Vector Machines, one of the new techniques for pattern classifi cation, have been widely used in many application areas. The kernel parameters setting for SVM in a training process impacts on the classifi cation accuracy. Feature selection is another factor that impacts classifi cation accuracy. The objective of this research is to simultaneously optimize the parameters and feature subset without degrading the SVM classifi cation accuracy. We present a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem.

A GA-based feature selection and parameters optimizationfor support vector machines.pdf

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