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基于ROC曲线寻优的支持向量机性能研究 被引量：9

Performance Evaluation with Optimization Strategy for Support Vector Machine Based on ROC Curve
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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,调整模型参数阈值描绘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。 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.
作者 王旭辉 舒平 曹立
机构地区 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 南京航空航天大学民航学院
出处 《计算机科学》 CSCD 北大核心 2010年第8期240-242,共3页 Computer Science
基金 国家高技术研究发展计划(863计划)(2006AA12A108) 国家自然科学基金(60879008)资助
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 Pattern recognition, Support vector machine, Parameter optimize, ROC curve
分类号 TP274 [自动化与计算机技术—检测技术与自动化装置] TP391 [自动化与计算机技术—计算机应用技术]
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参考文献12

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二级参考文献28

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共引文献155

同被引文献88

  • 1蔡晓龙,穆向阳,高炜欣,魏巍.基于PSO-SVM的焊缝缺陷X射线检测[J].焊接技术,2013,42(10):57-60. 被引量：6
  • 2蹇清平,艾志久,张勇,杨赟达.基于支持向量机的油管内外表面缺陷识别方法[J].机械科学与技术,2015,34(1):118-123. 被引量：9
  • 3林励,陈志霞,袁旭江,李向明.两种化橘红的质量鉴别[J].广州中医药大学学报,2004,21(4):308-312. 被引量：19
  • 4杨煦,张跃.基于支持向量机的心律失常识别方法[J].计算机工程与设计,2007,28(18):4442-4445. 被引量：6
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引证文献9

二级引证文献64

计算机科学
2010年 第8期

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