A Novel Kernel-based Gene Selection and Classification Scheme for Microarray Data

被引：0

作者：

Huang, Hsiao-Yun ^{[1
]}

Chang, Hui-Yi ^{[1
]}

Liu, Jeng-Fu ^{[1
]}

机构：

[1] Fu Jen Catholic Univ, Dept Stat & Informat Sci, New Taipei, Taiwan

来源：

6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS | 2012年

关键词：

SVM; varialbe importance; microarray; gene selection; classificaiton; TUMOR;

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：

Classification is one of the most important issues in microarray data analysis. Due to the SSS problem and other properties of microarray data, how to select the differentially expressed genes and how a build a proper classification model according these selected genes are two crucial concerns in constructing a powerful classification scheme. In this study, a classification scheme named SVMSC is proposed. SVMSC adopts a variable importance measure that is directly derived from RBF kernel function for selecting genes. This kernel function will also be used in the following SVM classifier. Since both the gene selection and classification are based on the same kernel function, SVMSC can properly express the capability of SVM. By comparing to several other popular classifiers with several different data sets, the experiment results showed that most of the best performances are associated with SVMSC.

引用

页码：1679 / 1683

页数：5

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