A feature selection method using improved regularized linear discriminant analysis

被引：0
|
作者：
Alok Sharma
Kuldip K. Paliwal
Seiya Imoto
Satoru Miyano
机构：
[1] University of Tokyo,Laboratory of DNA Information Analysis
[2] Griffith University,School of Engineering
[3] University of the South Pacific,School of Engineering and Physics
来源：
Machine Vision and Applications | 2014年 / 25卷
关键词：
Linear discriminant analysis (LDA); Regularized LDA; Feature/gene selection; Classification accuracy;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Investigation of genes, using data analysis and computer-based methods, has gained widespread attention in solving human cancer classification problem. DNA microarray gene expression datasets are readily utilized for this purpose. In this paper, we propose a feature selection method using improved regularized linear discriminant analysis technique to select important genes, crucial for human cancer classification problem. The experiment is conducted on several DNA microarray gene expression datasets and promising results are obtained when compared with several other existing feature selection methods.
引用
收藏
页码：775 / 786
页数：11
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