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Interval probabilistic neural network
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 n...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5362677/ https://www.ncbi.nlm.nih.gov/pubmed/28386161 http://dx.doi.org/10.1007/s00521-015-2109-3 |
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author | Kowalski, Piotr A. Kulczycki, Piotr |
author_facet | Kowalski, Piotr A. Kulczycki, Piotr |
author_sort | Kowalski, Piotr A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5362677 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-53626772017-04-04 Interval probabilistic neural network Kowalski, Piotr A. Kulczycki, Piotr Neural Comput Appl Original Article 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. Springer London 2015-11-21 2017 /pmc/articles/PMC5362677/ /pubmed/28386161 http://dx.doi.org/10.1007/s00521-015-2109-3 Text en © The Author(s) 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article Kowalski, Piotr A. Kulczycki, Piotr Interval probabilistic neural network |
title | Interval probabilistic neural network |
title_full | Interval probabilistic neural network |
title_fullStr | Interval probabilistic neural network |
title_full_unstemmed | Interval probabilistic neural network |
title_short | Interval probabilistic neural network |
title_sort | interval probabilistic neural network |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5362677/ https://www.ncbi.nlm.nih.gov/pubmed/28386161 http://dx.doi.org/10.1007/s00521-015-2109-3 |
work_keys_str_mv | AT kowalskipiotra intervalprobabilisticneuralnetwork AT kulczyckipiotr intervalprobabilisticneuralnetwork |