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Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data

Saini, Harsh and Lal, Sunil P. and Naidu, Vimal V. and Pickering, Vincel W. and Singh, Gurmeet and Tsunoda, Tatsuhiko and Sharma, Alokanand (2016) Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data. BMC Medical Genomics, 9 (3). pp. 74-83. ISSN 1755-8794

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    Abstract

    Background: High dimensional feature space generally degrades classification in several applications. In this paper, we propose a strategy called gene masking, in which non-contributing dimensions are heuristically removed from the data to improve classification accuracy. Methods: Gene masking is implemented via a binary encoded genetic algorithm that can be integrated seamlessly with classifiers during the training phase of classification to perform feature selection. It can also be used to discriminate between features that contribute most to the classification, thereby, allowing researchers to isolate features that may have special significance. Results: This technique was applied on publicly available datasets whereby it substantially reduced the number of features used for classification while maintaining high accuracies. Conclusion: The proposed technique can be extremely useful in feature selection as it heuristically removes non-contributing features to improve the performance of classifiers

    Item Type: Journal Article
    Subjects: Q Science > Q Science (General)
    R Medicine > R Medicine (General)
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
    Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
    Depositing User: Fulori Nainoca
    Date Deposited: 08 Mar 2017 16:41
    Last Modified: 08 Mar 2017 16:41
    URI: http://repository.usp.ac.fj/id/eprint/9665
    UNSPECIFIED

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