Adaptive feature selection via a new version of support vector machine

被引：26
|
作者：
Tan, Junyan [1 ]
Zhang, Zhiqiang [2 ]
Zhen, Ling [1 ]
Zhang, Chunhua [3 ]
Deng, Naiyang [1 ]
机构：
[1] China Agr Univ, Coll Sci, Beijing 100083, Peoples R China
[2] Beijing Inst Technol, Sch Mech & Vehicular Engn, Beijing 100081, Peoples R China
[3] Renmin Univ China, Informat Sch, Dept Math, Beijing 100872, Peoples R China
来源：
NEURAL COMPUTING & APPLICATIONS | 2013年 / 23卷 / 3-4期
基金：
中国国家自然科学基金;
关键词：
Support vector machine; Feature selection; p-norm; GENE SELECTION; CANCER; CLASSIFICATION;
D O I：
10.1007/s00521-012-1018-y
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper focuses on feature selection in classification. A new version of support vector machine (SVM) named p-norm support vector machine () is proposed. Different from the standard SVM, the p-norm of the normal vector of the decision plane is used which leads to more sparse solution. Our new model can not only select less features but also improve the classification accuracy by adjusting the parameter p. The numerical experiments results show that our p-norm SVM is more effective than some usual methods in feature selection.
引用
收藏
页码：937 / 945
页数：9
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