A feature selection method using improved regularized linear discriminant analysis
File version
Accepted Manuscript (AM)
Author(s)
Paliwal, Kuldip K
Imoto, Seiya
Miyano, Satoru
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
Size
File type(s)
Location
License
Abstract
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.
Journal Title
Machine Vision and Applications
Conference Title
Book Title
Edition
Volume
25
Issue
3
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights 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.
Item Access Status
Note
Access the data
Related item(s)
Subject
Theory of computation
Cognitive and computational psychology