A METHOD FOR FEATURE SELECTION BASED ON THE OPTIMAL HYPERPLANE OF SVM AND INDEPENDENT ANALYSIS

被引：0

作者：

Hu, Lin-Fang ^{[1
]}

Gong, Wei ^{[1
]}

Qi, Li-Xiao ^{[1
]}

Wang, Ping ^{[2
]}

机构：

[1] Tianjin Inst Urban Construct, Sch Control & Mech Engn, Tianjin 300384, Peoples R China

[2] Tianjin Univ, Inst Elect Engn & Automat, Tianjin 300072, Peoples R China

来源：

PROCEEDINGS OF 2013 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOLS 1-4 | 2013年

关键词：

Feature Selection; The optimal hyperplane; Correlation analysis; Support vector machine; GENE SELECTION; CLASSIFICATION; INFORMATION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Feature selection is an important topic in machine learning. In order to evaluate the candidate features, a strategy based on the constituent principle of the SVM optimal hyperplane is established in this paper. Then, by considering different feature combinations, a better feature subset can be obtained. The method is used to recognize the monomers in weather forecast, and experimental results demonstrate its effectiveness in enhancing the classification performance.

引用

页码：114 / 117

页数：4

共 13 条

[11] Tabus I., 2001, EURASIP Journal on Applied Signal Processing, V2001, P297, DOI 10.1155/S1110865701000270
[12] A Bayesian approach to nonlinear probit gene selection and classification
Zhou, XB
Wang, XD
Dougherty, ER
[J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2004, 341 (1-2): : 137 - 156
[13] Nonlinear probit gene classification using mutual information and wavelet-based feature selection
Zhou, XB
Wang, XD
Dougherty, ER
[J]. JOURNAL OF BIOLOGICAL SYSTEMS, 2004, 12 (03) : 371 - 386

← 1 2 →