基于ROC曲线寻优的支持向量机性能研究

计算机科学 ›› 2010, Vol. 37 ›› Issue (8): 240-242.

基于ROC曲线寻优的支持向量机性能研究

王旭辉，舒平，曹立

(中国民用航空总局航空安全技术中心航空安全技术实验室北京100028),(南京航空航天大学民航学院南京210016)

出版日期:2018-12-01 发布日期:2018-12-01
基金资助:
本文受国家高技术研究发展计划(863计划)(2006AA12A108)和国家自然科学基金(60879008)资助。

Performance Evaluation with Optimization Strategy for Support Vector Machine Based on ROC Curve

WANG Xu-hui,SHU Ping,CAO Li

Online:2018-12-01 Published:2018-12-01

摘要/Abstract

摘要： 支持向量机在小样本模式识别领域具有优势，但其性能评估及核参数、正则化参数的选择尚未有标准算法。将受试者操作特性曲线(Receiver Operating Characteristic, ROC)引入支持向量机分类性能分析和建模参数优化问题。在核参数及正则化参数所构成的二维空间中，调整模型参数阂值描绘ROC曲线，通过比较不同分类器ROC曲线下面积实现模型的性能分析，研究了基于ROC曲线最佳工作点的模型优化问题。工程实例表明，ROC曲线下面积有效地量化了模型的识别性能，并给出了一定寻优范围内的模型参数最优点，可以在SVM模型参数优化问题中推广应用。

关键词: 模式识别，支持向量机，参数优化，受试者操作特性曲线

Abstract: 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.

Key words: Pattern recognition, Support vector machine, Parameter optimize, ROC curve

王旭辉，舒平，曹立. 基于ROC曲线寻优的支持向量机性能研究[J]. 计算机科学, 2010, 37(8): 240-242. https://doi.org/

WANG Xu-hui,SHU Ping,CAO Li. Performance Evaluation with Optimization Strategy for Support Vector Machine Based on ROC Curve[J]. Computer Science, 2010, 37(8): 240-242. https://doi.org/