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    On two measures of classifier competence for dynamic ensemble selection - Experimental comparative analysis

    Access Status
    Fulltext not available
    Authors
    Kurzynski, M.
    Woloszynski, Tomasz
    Lysiak, R.
    Date
    2010
    Type
    Conference Paper
    
    Metadata
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    Citation
    Kurzynski, M. and Woloszynski, T. and Lysiak, R. 2010. On two measures of classifier competence for dynamic ensemble selection - Experimental comparative analysis, pp. 1108-1113.
    Source Title
    ISCIT 2010 - 2010 10th International Symposium on Communications and Information Technologies
    DOI
    10.1109/ISCIT.2010.5665153
    ISBN
    9781424470105
    School
    Department of Mechanical Engineering
    URI
    http://hdl.handle.net/20.500.11937/38320
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents two methods for calculating competence of a classifier in the feature space. The idea of the first method is based on relating the response of the classifier with the response obtained by a random guessing. The measure of competence reflects this relation and rates the classifier with respect to the random guessing in a continuous manner. In the second method, first a probabilistic reference classifier (PRC) is constructed which, on average, acts like the classifier evaluated. Next the competence of the classifier evaluated is calculated as the probability of correct classification of the respective PRC. Two multiclassifier systems (MCS) were developed using proposed measures of competence in a dynamic fashion. The performance of proposed MCS's were compared against six multiple classifier systems using six databases taken from the UCI Machine Learning Repository and Ludmila Kuncheva Collection. The experimental results clearly show the effectiveness of the proposed dynamic selection methods regardless of the ensamble type used (homogeneous or heterogeneous). ©2010 IEEE.

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