An Improved SVM Based on Feature Extension and Feature Selection

作者：Wei Feng, Yao; Xiao Bao, Jia

来源：Applied Mechanics and Materials, 2014, 552: 128-132.

DOI：10.4028/www.scientific.net/amm.552.128

<jats:p>Support Vector Machine (SVM) implicitly maps samples from the lower-dimensional feature space to a higher-dimensional space, and designs a non-linear classifier via optimize the linear classifier in the higher-dimensional space. This paper proposed an improved SVM method based on feature extension and feature selection. The method explicitly maps the samples to a higher-dimensional feature space, perform the feature selection in the space, and finally design a linear classifier with a selected feature set. This paper illustrated the reason why the generalization ability is improved by this technique. The experiment results on benchmark datasets show that the improved SVM greatly decreases the error rate compared with other classifiers, which proves the feasibility of the proposed SVM.</jats:p>

• 单位
  深圳职业技术学院