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    A measure of competence based on random classification for dynamic ensemble selection

    Access Status
    Fulltext not available
    Authors
    Woloszynski, Tomasz
    Kurzynski, M.
    Podsiadlo, Pawel
    Stachowiak, Gwidon
    Date
    2012
    Type
    Journal Article
    
    Metadata
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    Citation
    Woloszynski, Tomasz and Kurzynski, Marek and Podsiadlo, Pawel and Stachowiak, Gwidon W. 2012. A measure of competence based on random classification for dynamic ensemble selection. Information Fusion. 13 (3): pp. 207-213.
    Source Title
    Information Fusion
    DOI
    10.1016/j.inffus.2011.03.007
    ISSN
    1566-2535
    URI
    http://hdl.handle.net/20.500.11937/36540
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, a measure of competence based on random classification (MCR) for classifier ensembles is presented. The measure selects dynamically (i.e. for each test example) a subset of classifiers from the ensemble that perform better than a random classifier. Therefore, weak (incompetent) classifiers that would adversely affect the performance of a classification system are eliminated. When all classifiers in the ensemble are evaluated as incompetent, the classification accuracy of the system can be increased by using the random classifier instead. Theoretical justification for using the measure with the majority voting rule is given. Two MCR based systems were developed and their performance was compared against six multiple classifier systems using data sets taken from the UCI Machine Learning Repository and Ludmila Kuncheva Collection. The systems developed had typically the highest classification accuracies regardless of the ensemble type used (homogeneous or heterogeneous).

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