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    Optimal selection of ensemble classifiers using measures of competence and diversity of base classifiers

    Access Status
    Fulltext not available
    Authors
    Lysiak, R.
    Kurzynski, M.
    Woloszynski, Tomasz
    Date
    2014
    Type
    Journal Article
    
    Metadata
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    Citation
    Lysiak, R. and Kurzynski, M. and Woloszynski, T. 2014. Optimal selection of ensemble classifiers using measures of competence and diversity of base classifiers. Neurocomputing. 126: pp. 29-35.
    Source Title
    Neurocomputing
    DOI
    10.1016/j.neucom.2013.01.052
    ISSN
    0925-2312
    URI
    http://hdl.handle.net/20.500.11937/16870
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, a new probabilistic model using measures of classifier competence and diversity is proposed. The multiple classifier system (MCS) based on the dynamic ensemble selection scheme was constructed using both developed measures. Two different optimization problems of ensemble selection are defined and a solution based on the simulated annealing algorithm is presented. The influence of minimum value of competence and diversity in the ensemble on classification performance was investigated. The effectiveness of the proposed dynamic selection methods and the influence of both measures were tested using seven databases taken from the UCI Machine Learning Repository and the StatLib statistical dataset. Two types of ensembles were used: homogeneous or heterogeneous. The results show that the use of diversity positively affects the quality of classification. In addition, cases have been identified in which the use of this measure has the greatest impact on quality.

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