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

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    Author(s)
    Saini, Harsh
    Lal, Sunil Pranit
    Naidu, Vimal Vikash
    Pickering, Vincel Wince
    Singh, Gurmeet
    Tsunoda, Tatsuhiko
    Sharma, Alok
    Griffith University Author(s)
    Sharma, Alok
    Year published
    2016
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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 ...
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    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.
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    Journal Title
    BMC Medical Genomics
    Volume
    9
    Issue
    74
    DOI
    https://doi.org/10.1186/s12920-016-0233-2
    Copyright Statement
    © The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/ zero/1.0/) applies to the data made available in this article, unless otherwise stated.
    Subject
    Genetics
    Genetics not elsewhere classified
    Medical biochemistry and metabolomics
    Oncology and carcinogenesis
    Publication URI
    http://hdl.handle.net/10072/101149
    Collection
    • Journal articles

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