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    A measure of competence based on randomized reference classifier for dynamic ensemble selection

    Access Status
    Fulltext not available
    Authors
    Woloszynski, Tomasz
    Kurzynski, M.
    Date
    2010
    Type
    Conference Paper
    
    Metadata
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    Citation
    Woloszynski, T. and Kurzynski, M. 2010. A measure of competence based on randomized reference classifier for dynamic ensemble selection, pp. 4194-4197.
    Source Title
    Proceedings - International Conference on Pattern Recognition
    DOI
    10.1109/ICPR.2010.1019
    ISBN
    9780769541099
    School
    Department of Mechanical Engineering
    URI
    http://hdl.handle.net/20.500.11937/37118
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents a measure of competence based on a randomized reference classifier (RRC) for classifier ensembles. The RRC can be used to model, in terms of class supports, any classifier in the ensemble. The competence of a modelled classifier is calculated as the probability of correct classification of the respective RRC. A multiple classifier system (MCS) was developed and its performance was compared against five MCSs using eight databases taken from the UCI Machine Learning Repository. The system developed achieved the highest overall classification accuracies for both homogeneous and heterogeneous ensembles. © 2010 IEEE.

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