A ROC Curve Method for Performance Evaluation of Support Vector Machine with Optimization Strategy
Support vector machine is the highlight in machine learning. Also, performance evaluation and parameters selection for SVM model become an important issue to make it practically useful. In this paper, after investigating current evaluation index for pattern recognition, we introduced Receiver Operating Characteristic Curve into the performance evaluation. Area under Receiver Operating Characteristic Curve is applied to the model evaluation, model performance of SVM and RBFN is compared. Also optimal operating point of ROC is adopted to the optimization of SVM within the kernel parameters and penalty factor, and the optimization is performed by seeking of optimal operating point. Pattern recognition experiment with UCI dataset shows that ROC method is an effective approach for performance evaluation and optimization of SVM.
pattern recognition support vector machine parameter optimize ROC curve
Wang Xu-hui Shu Ping Cao Li Wang Ye
Aviation Safety Institute, Center of Aviation Safety Technology, CAAC,Beijing, China College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China
国际会议
重庆
英文
599-602
2009-12-25（万方平台首次上网日期，不代表论文的发表时间）