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Presupervised and post-supervised prototype classifier design.

L I Kuncheva1, J C Bezdek

  • 1School of Mathematics, University of Wales, Bangor, Bangor, Gwynedd LL57 1UT, UK.

IEEE Transactions on Neural Networks
|February 7, 2008
PubMed
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We introduce the generalized nearest prototype classifier (GNPC) with soft class labels, offering a flexible approach to classification. Both presupervised and postsupervised GNPC designs show comparable performance, with postsupervised being more robust.

Area of Science:

  • Machine Learning
  • Pattern Recognition
  • Data Mining

Background:

  • Nearest prototype classifiers are fundamental in pattern recognition.
  • Existing methods often rely on hard assignments of prototypes to classes.

Purpose of the Study:

  • To generalize the nearest prototype classifier using soft class labeling.
  • To introduce and analyze presupervised and postsupervised GNPC designs.
  • To determine conditions for optimality relative to the Bayes error rate.

Main Methods:

  • Extension of the nearest prototype classifier to a generalized version (GNPC).
  • Development of presupervised and postsupervised GNPC designs based on prototype selection.
  • Derivation of optimality conditions for GNPC designs.

Related Experiment Videos

  • Experimental validation using artificial and real-world datasets (satimage).
  • Implementation using radial basis function (RBF) networks.

    • Main Results:

    • Two GNPC designs were derived: presupervised (prototypes from class-conditional densities) and postsupervised (prototypes from unconditional density).
    • Optimality conditions relative to Bayes error rate were established for both designs.
    • Experimental results on artificial and satimage datasets did not show a clear preference for either approach.
    • The postsupervised GNPC demonstrated higher robustness but lower accuracy compared to the presupervised GNPC.

    Conclusions:

    • The generalized nearest prototype classifier (GNPC) offers a versatile framework encompassing various classifiers through soft labeling.
    • Both presupervised and postsupervised GNPC designs are theoretically sound, with no definitive advantage identified.
    • The choice between presupervised and postsupervised GNPC may depend on the desired trade-off between robustness and accuracy.