文件名称:A-GA-based-feature-selection-and-parameters-optim
- 所属分类：
- 人工智能/神经网络/遗传算法
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- [PDF]
- 上传时间：
- 2012-11-26
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- 138kb
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- pa***
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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.
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.
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A GA-based feature selection and parameters optimizationfor support vector machines.pdf