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  • A filter based feature selection algorithm using null space of covariance matrix for DNA microarray gene expression data

    Author(s)
    Sharma, Alok
    Imoto, Seiya
    Miyano, Satoru
    Griffith University Author(s)
    Sharma, Alok
    Year published
    2012
    Metadata
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    Abstract
    We propose a new filter based feature selection algorithm for classification based on DNA microarray gene expression data. It utilizes null space of covariance matrix for feature selection. The algorithm can perform bulk reduction of features (genes) while maintaining the quality information in the reduced subset of features for discriminative purpose. Thus, it can be used as a pre-processing step for other feature selection algorithms. The algorithm does not assume statistical independency among the features. The algorithm shows promising classification accuracy when compared with other existing techniques on several DNA ...
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    We propose a new filter based feature selection algorithm for classification based on DNA microarray gene expression data. It utilizes null space of covariance matrix for feature selection. The algorithm can perform bulk reduction of features (genes) while maintaining the quality information in the reduced subset of features for discriminative purpose. Thus, it can be used as a pre-processing step for other feature selection algorithms. The algorithm does not assume statistical independency among the features. The algorithm shows promising classification accuracy when compared with other existing techniques on several DNA microarray gene expression datasets.
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    Journal Title
    Current Bioinformatics
    Volume
    7
    Issue
    3
    DOI
    https://doi.org/10.2174/157489312802460802
    Subject
    Mathematical sciences
    Biological sciences
    Evolution of developmental systems
    Information and computing sciences
    Publication URI
    http://hdl.handle.net/10072/51801
    Collection
    • Journal articles

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