Title

G-Pnn: A Genetically Engineered Probabilistic Neural Network

Keywords

Bayesian classifier; Genetic algorithms; Optimization; Parzen window; Probabilistic neural network; Smoothing parameters

Abstract

The probabilistic neural network (PNN) is a neural network architecture that approximates the functionality of the Bayesian classifier, the optimal classifier. Designing the optimal Bayesian classifier is infeasible in practice, since the distributions 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 issues (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. © 2010 Elsevier Ltd. All rights reserved.

Publication Date

9-15-2010

Publication Title

Nonlinear Analysis, Theory, Methods and Applications

Volume

73

Issue

6

Number of Pages

1783-1791

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.na.2010.04.080

Socpus ID

77953959917 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77953959917

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