Feature Rescaling of Support Vector Machines  

作　　者：武征鹏 张学工 

机构地区：[1]MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Department of Automation, Tsinghua University

出　　处：《Tsinghua Science and Technology》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)

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.

关 键 词：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]