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    Combining classifiers in a tree structure

    Access Status
    Fulltext not available
    Authors
    Woloszynski, Tomasz
    Kurzynski, M.
    Date
    2008
    Type
    Conference Paper
    
    Metadata
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    Citation
    Woloszynski, T. and Kurzynski, M. 2008. Combining classifiers in a tree structure, pp. 785-790.
    Source Title
    2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008
    DOI
    10.1109/CIMCA.2008.22
    ISBN
    9780769535142
    School
    Department of Mechanical Engineering
    URI
    http://hdl.handle.net/20.500.11937/36035
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents a method for combining classifiers in a tree structure, where each node of the tree contains single hypothesis trained in respective region of the feature space. All base classifiers are then combined using weighted average. Majority vote and Newton-Raphson numerical optimization are used for fitting the coefficients in the additive model. Two loss functions (quadratic and boosting-like exponential) as well as new splitting criteria for inducing the tree are examined within proposed framework. The idea of combining classifiers in a tree structure is then compared with other iteratively built classifiers: Adaboost.MH and MART (multiple additive regression trees). The experiments were conducted with the usage of well-known databases from the UCI Repository and the ELENA project. © 2008 IEEE.

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