G-PNN: A genetically engineered probabilistic neural network

    Authors

    R. Miguez; M. Georgiopoulos;A. Kaylani

    Comments

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    Abbreviated Journal Title

    Nonlinear Anal.-Theory Methods Appl.

    Keywords

    Bayesian classifier; Probabilistic neural network; Parzen window; Smoothing parameters; Genetic algorithms; Optimization; DENSITY; CLASSIFICATION; Mathematics, Applied; Mathematics

    Abstract

    The probabilistic neural network (PNN) is a neural architecture that approximates the functionality o fthe Bayesian classifier, the optimal classifier. Designing the optimal Bayesian classifier is infeasible in practice, since the distribution of data belonging to each class are unknown. PNN is an approximation of the Bayesian classifier by approximating these distributions using the Parzen window approach. One of the criticisms of the PNN classifier is that, at times, it uses a lot of training data for its design. Furthermore, the PNN classifier requires that the user specifies certain network parameters, called the smoothing (spread) parameters, in order to approximate the distributions of the class data, which is not an easy task. A number of approaches have been reported in the literature for addressing both of these (i.e., reducing the number of training data needed for the building of the PNN model and producing good values for the smoothing parameters). In this effort, genetic algorithms are used to achieve both goals at once, and some promising results are reported. (C) Elsevier Ltd. All rights reserved.

    Journal Title

    Nonlinear Analysis-Theory Methods & Applications

    Volume

    73

    Issue/Number

    6

    Publication Date

    1-1-2010

    Document Type

    Article

    Language

    English

    First Page

    1783

    Last Page

    1791

    WOS Identifier

    WOS:000280220200028

    ISSN

    0362-546X

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