Performance Evaluation with Optimization Strategy for Support Vector Machine Based on ROC Curve
基于ROC曲线寻优的支持向量机性能研究

Support vector machine (SVM) has become a popular tool in the area of pattern recognition, and parameters selection for SVM is an important issue to make it practically useful. In this paper, we introduced Receiver Operating Characteristic Curve into the performance evaluation and model optimization of SVM within the kernel parameters s and penalty factor c. Area under ROC curve was applied to the model evaluation, and model optimization was performed by seeking of optimal operating point of ROC. Pattern recognition experiment with UCI dataset shows that ROC curve is an effective approach for performance evaluation and optimization of SVM.