Microarray gene expression classification based on supervised learning and similarity measures

被引：3

作者：

Liu, Qingzhong ^{[1
]}

Sung, Andrew H. ^{[1
,2
]}

Xu, Jianyun ^{[3
]}

Liu, Jianzhong ^{[4
]}

Chen, Zhongxue ^{[5
]}

机构：

[1] New Mexico Inst Min & Technol, Dept Comp Sci, Socorro, NM 87801 USA

[2] New Mexico Inst Min & Technol, Inst Complex Addit Syst Anal, Socorro, NM 87801 USA

[3] Microsoft Corp, Redmond, WA 98052 USA

[4] Univ Delaware, Dept Chem & Biochem, Newark, DE 19716 USA

[5] So Methodist Univ, Dept Chem & Biochem, Dallas, TX 75275 USA

来源：

2006 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-6, PROCEEDINGS | 2006年

关键词：

D O I：

10.1109/ICSMC.2006.385116

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Microarray gene expression data has high dimension and small samples, the gene selection is very important to the classification accuracy. In this paper, we present a scheme of recursive feature addition for microarray gene expression classification based on supervised learning and the similarity measure between chosen genes and candidates. In comparison with the well-known gene selection methods of T-TEST and SVM-RFE using different classifiers, our method, on the average, performs the best regarding the classification accuracy under different feature dimensions, the mean test accuracy and the highest test accuracy under the highest train accuracy, and the highest test accuracy in the experiments.

引用

页码：5094 / +

页数：3

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