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  • A feature selection method using improved regularized linear discriminant analysis

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    Accepted Manuscript (AM)
    Author(s)
    Sharma, Alok
    Paliwal, Kuldip K
    Imoto, Seiya
    Miyano, Satoru
    Griffith University Author(s)
    Paliwal, Kuldip K.
    Year published
    2014
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    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.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.
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    Journal Title
    Machine Vision and Applications
    Volume
    25
    Issue
    3
    DOI
    https://doi.org/10.1007/s00138-013-0577-y
    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
    Publication URI
    http://hdl.handle.net/10072/108776
    Collection
    • Journal articles

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