G-PNN: A genetically engineered probabilistic neural network
Abbreviated Journal Title
Nonlinear Anal.-Theory Methods Appl.
Bayesian classifier; Probabilistic neural network; Parzen window; Smoothing parameters; Genetic algorithms; Optimization; DENSITY; CLASSIFICATION; Mathematics, Applied; Mathematics
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.
Nonlinear Analysis-Theory Methods & Applications
"G-PNN: A genetically engineered probabilistic neural network" (2010). Faculty Bibliography 2010s. 541.