A feature selection method using improved regularized linear discriminant analysis

View/ Open
File version
Accepted Manuscript (AM)
Author(s)
Sharma, Alok
Paliwal, Kuldip K
Imoto, Seiya
Miyano, Satoru
Griffith University Author(s)
Year published
2014
Metadata
Show full item recordAbstract
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.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.
View less >
View less >
Journal Title
Machine Vision and Applications
Volume
25
Issue
3
Copyright Statement
© 2013 Springer Berlin Heidelberg. This is an electronic version of an article published in Machine Vision and Applications, April 2014, Volume 25, Issue 3, pp 775-786. Machine Vision and Applications is available online at: http://link.springer.com/ with the open URL of your article.
Subject
Theory of computation
Cognitive and computational psychology