¹ College of Science, Nanjing University of Aeronautics and Astronautics, Nanjing, China
² School of Physics & Electronic Engineering, Anqing Normal College, Anqing, China
³ Institute of Machine Vision and Machine Intelligence, Beijing Jingwei Textile Machinery New Technology Co., Ltd., Beijing, China
⁴ Jiangsu Institute of Standardization, Nanjing, China
⁵ The Second Affiliated Hospital, Anhui Medical University, China

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Abstract—A novel method of feature selection combined with sample selection is proposed to select discriminant features in this paper. Based on support vector machine trained on training set, the samples excluding the misclassified samples and support vector samples are used to select informative features during the procedure of recursive feature selection. The feature selection method is applied to seven datasets, and the classification results of the selected discriminant features show that the method is effective and reliable for selecting features with high classification information.

Index Terms—Discriminant feature selection, Support Vector Machine, Sample selection, Gene expression profile

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Cite: Quanjin Liu, Zhimin Zhao, Yingxin Li, Xiaolei Yu, and Yong Wang, " A Novel Method of Feature Selection based on SVM," Journal of Computers vol. 8, no. 8, pp. 2144-2149, 2013.