Comparative study of feature selection methods on microarray data

被引：2

作者：

Miyamoto, T ^{[1
]}

Uchimura, S ^{[1
]}

Hamamoto, Y ^{[1
]}

Iizuka, N ^{[1
]}

Oka, M ^{[1
]}

Yamada-Okabe, H ^{[1
]}

机构：

[1] Yamaguchi Univ, Fac Engn, Dept Comp Sci & Syst Engn, Yamaguchi, Japan

来源：

IEEE EMBS APBME 2003 | 2003年

关键词：

supervised statistical pattern recognition; feature selection; microarray data;

D O I：

10.1109/APBME.2003.1302594

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

It is difficult to apply usual statistical pattern recognition techniques directly to microarray data, because the number of genes is too large in comparison with the number of available training samples. Therefore, one needs 2 powerful feature selection method for microarray data. In this paper, we compare the previously published feature selection method with the sequential forward selection (SFS) method and the Fisher criterion-based feature selection method on the microarray data of hepatocellular carcinoma (http://surgery2.med.yamaguchi-u.ac.jp/research/DNAchip/). Experimental results show that our method outperforms the SFS method and the Fisher criterion-based method in terms of the recognition rate.

引用

页码：82 / 83

页数：2

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