A feature selection method using improved regularized linear discriminant analysis

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• 作者：Alok Sharma (1) (2) (3)
  Kuldip K. Paliwal (2)
  Seiya Imoto (1)
  Satoru Miyano (1)
  
• 关键词：Linear discriminant analysis (LDA) ; Regularized LDA ; Feature/gene selection ; Classification accuracy
• 刊名：Machine Vision and Applications
• 出版年：2014
• 出版时间：April 2014
• 年：2014
• 卷：25
• 期：3
• 页码：775-786
• 全文大小：674 KB
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• 作者单位：Alok Sharma (1) (2) (3)
  Kuldip K. Paliwal (2)
  Seiya Imoto (1)
  Satoru Miyano (1)
  
  1. Laboratory of DNA Information Analysis, University of Tokyo, Tokyo, Japan
  2. School of Engineering, Griffith University, Brisbane, Australia
  3. School of Engineering and Physics, University of the South Pacific, Suva, Fiji
  
• ISSN：1432-1769

文摘

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.