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Feature Rescaling of Support Vector Machines 被引量：3

Feature Rescaling of Support Vector Machines
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摘要 Support vector machines （SVMs） have widespread use in various classification problems. Although SVMs are often used as an off-the-shelf tool, there are still some important issues which require improvement such as feature rescaling. Standardization is the most commonly used feature rescaling method. However, standardization does not always improve classification accuracy. This paper describes two feature rescaling methods： multiple kernel learning-based rescaling （MKL-SVM） and kernel-target alignment-based rescaling （KTA-SVM）. MKL-SVM makes use of the framework of multiple kernel learning （MKL） and KTA-SVM is built upon the concept of kernel alignment, which measures the similarity between kernels. The proposed meth- ods were compared with three other methods： an SVM method without rescaling, an SVM method with standardization, and SCADSVM. Test results demonstrate that different rescaling methods apply to different situations and that the proposed methods outperform the others in general. Support vector machines （SVMs） have widespread use in various classification problems. Although SVMs are often used as an off-the-shelf tool, there are still some important issues which require improvement such as feature rescaling. Standardization is the most commonly used feature rescaling method. However, standardization does not always improve classification accuracy. This paper describes two feature rescaling methods： multiple kernel learning-based rescaling （MKL-SVM） and kernel-target alignment-based rescaling （KTA-SVM）. MKL-SVM makes use of the framework of multiple kernel learning （MKL） and KTA-SVM is built upon the concept of kernel alignment, which measures the similarity between kernels. The proposed meth- ods were compared with three other methods： an SVM method without rescaling, an SVM method with standardization, and SCADSVM. Test results demonstrate that different rescaling methods apply to different situations and that the proposed methods outperform the others in general.
作者 武征鹏 张学工
机构地区 MOE Key Laboratory of Bioinformatics and Bioinformatics Division
出处 《Tsinghua Science and Technology》 SCIE EI CAS 2011年第4期414-421,共8页 清华大学学报（自然科学版（英文版）
基金 Supported by the National Natural Science Foundation of China(Nos. 30625012 and 60721003)
关键词 support vector machines （SVMs） feature rescaling multiple kernel learning （MKL） kernel-targetalignment （KTA） support vector machines （SVMs） feature rescaling multiple kernel learning （MKL） kernel-targetalignment （KTA）
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献17

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同被引文献30

  • 1常玉清,邹伟,王福利,毛志忠.基于支持向量机的软测量方法研究[J].控制与决策,2005,20(11):1307-1310. 被引量：18
  • 2刘毅,王海清.采用最小二乘支持向量机的青霉素发酵过程建模研究[J].生物工程学报,2006,22(1):144-149. 被引量：27
  • 3刘毅,王海清.Pensim仿真平台在青霉素发酵过程的应用研究[J].系统仿真学报,2006,18(12):3524-3527. 被引量：44
  • 4边肇祺,张学工.模式识别[M].2版.北京:清华大学出版社,2002.
  • 5DAVENPORT M A, BARANIUK R G, scorT C D. Tuning support vector machines for minimax and Neyman-Pearson classification [ J ]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2010,32 (10) : 1888-1898.
  • 6PAL M, FOODY G M. Feature selection for classification of hyper spectral data by SVM [J]. IEEE Trans on Geoscience and Re- mote SenSing, 2010,48(5) : 229?-2307.
  • 7TSANG I W, KOCSOR A, KWOK J T. Large-scale maximum margin discriminant analysis using core vector machines [ J]. IEEE Trans on Neural Networks, 2008,t9(4) : 610-624.
  • 8DENG Zhao-hong, CHUNG Fu-lai, WANG Shi-tong. FRSDE: fast reduced set density estimator using minimal enclosing ball approxima- tion[ J]. Patternon Recognition, 2008,41 (4) :1363-1372.
  • 9DENG Zhao-hong CHOI K S, CHUNG Fu-lai, et al. Scalable TSK fuzzy modeling for very large datasets using minimal enclosing ball ap- proximation[J]. IEEE Trans Fuzzy Systems, 2011,19(2) : 210- 226.
  • 10HUANG Guang-bin, ZHU Qin-yu, SlEW C K. Extreme learning ma- chine : theory and applications [ J ]. Neurocomputing, 2006,70 ( 1- 3) : 489-501.

引证文献3

二级引证文献2

Tsinghua Science and Technology
2011年 第4期

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