Affiliations: Department of Civil and Environmental Engineering, Southern Methodist University, Dallas, TX, USA
Correspondence:
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Corresponding author: Brett A. Story, Department of Civil and Environmental Engineering, Southern Methodist University, 3101 Dyer Street, Embrey Building, Rm 203, Dallas, TX 75205, USA. Tel.: +1 214 768 1991; E-mail:bstory@lyle.smu.edu
Abstract: Probabilistic neural networks (PNNs) are artificial
neural network algorithms widely used in pattern recognition and
classification problems. In the traditional PNN algorithm, the probability
density function (PDF) is approximated using the entire training dataset for
each class. In some complex datasets, classmate clusters may be located far
from each other and these distances between clusters may cause a reduction
in the correct class's posterior probability and lead to misclassification.
This paper presents a novel PNN algorithm, the competitive probabilistic
neural network (CPNN). In the CPNN, a competitive layer ranks kernels for
each class and an optimum fraction of kernels are selected to estimate the
class-conditional probability. Using a stratified, repeated, random
subsampling cross-validation procedure and 9 benchmark classification
datasets, CPNN is compared to both traditional PNN and the state of the art
(e.g. enhanced probabilistic neural network, EPNN). These datasets are
examined with and without noise and the algorithm is evaluated with several
ratios of training to testing data. In all datasets (225 simulation
categories), performance percentages of both CPNN and EPNN are greater than
or equivalent to that of the traditional PNN; in 73% of simulation
categories, the CPNN analyses show modest improvement in performance over
the state of the art.
