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2017 ; 28
(4
): 817-834
Nephropedia Template TP
Neural Comput Appl
2017[]; 28
(4
): 817-834
PMID28386161
show ga
Automated classification systems have allowed for the rapid development of
exploratory data analysis. Such systems increase the independence of human
intervention in obtaining the analysis results, especially when inaccurate
information is under consideration. The aim of this paper is to present a novel
approach, a neural networking, for use in classifying interval information. As
presented, neural methodology is a generalization of probabilistic neural network
for interval data processing. The simple structure of this neural classification
algorithm makes it applicable for research purposes. The procedure is based on
the Bayes approach, ensuring minimal potential losses with regard to that which
comes about through classification errors. In this article, the topological
structure of the network and the learning process are described in detail. Of
note, the correctness of the procedure proposed here has been verified by way of
numerical tests. These tests include examples of both synthetic data, as well as
benchmark instances. The results of numerical verification, carried out for
different shapes of data sets, as well as a comparative analysis with other
methods of similar conditioning, have validated both the concept presented here
and its positive features.
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