Cargando…
Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks
Artificial neural networks are currently one of the most commonly used classifiers and over the recent years they have been successfully used in many practical applications, including banking and finance, health and medicine, engineering and manufacturing. A large number of error functions have been...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512764/ https://www.ncbi.nlm.nih.gov/pubmed/33265339 http://dx.doi.org/10.3390/e20040249 |
_version_ | 1783586233258082304 |
---|---|
author | Gajowniczek, Krzysztof Orłowski, Arkadiusz Ząbkowski, Tomasz |
author_facet | Gajowniczek, Krzysztof Orłowski, Arkadiusz Ząbkowski, Tomasz |
author_sort | Gajowniczek, Krzysztof |
collection | PubMed |
description | Artificial neural networks are currently one of the most commonly used classifiers and over the recent years they have been successfully used in many practical applications, including banking and finance, health and medicine, engineering and manufacturing. A large number of error functions have been proposed in the literature to achieve a better predictive power. However, only a few works employ Tsallis statistics, although the method itself has been successfully applied in other machine learning techniques. This paper undertakes the effort to examine the [Formula: see text]-generalized function based on Tsallis statistics as an alternative error measure in neural networks. In order to validate different performance aspects of the proposed function and to enable identification of its strengths and weaknesses the extensive simulation was prepared based on the artificial benchmarking dataset. The results indicate that Tsallis entropy error function can be successfully introduced in the neural networks yielding satisfactory results and handling with class imbalance, noise in data or use of non-informative predictors. |
format | Online Article Text |
id | pubmed-7512764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75127642020-11-09 Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks Gajowniczek, Krzysztof Orłowski, Arkadiusz Ząbkowski, Tomasz Entropy (Basel) Article Artificial neural networks are currently one of the most commonly used classifiers and over the recent years they have been successfully used in many practical applications, including banking and finance, health and medicine, engineering and manufacturing. A large number of error functions have been proposed in the literature to achieve a better predictive power. However, only a few works employ Tsallis statistics, although the method itself has been successfully applied in other machine learning techniques. This paper undertakes the effort to examine the [Formula: see text]-generalized function based on Tsallis statistics as an alternative error measure in neural networks. In order to validate different performance aspects of the proposed function and to enable identification of its strengths and weaknesses the extensive simulation was prepared based on the artificial benchmarking dataset. The results indicate that Tsallis entropy error function can be successfully introduced in the neural networks yielding satisfactory results and handling with class imbalance, noise in data or use of non-informative predictors. MDPI 2018-04-03 /pmc/articles/PMC7512764/ /pubmed/33265339 http://dx.doi.org/10.3390/e20040249 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gajowniczek, Krzysztof Orłowski, Arkadiusz Ząbkowski, Tomasz Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks |
title | Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks |
title_full | Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks |
title_fullStr | Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks |
title_full_unstemmed | Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks |
title_short | Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks |
title_sort | simulation study on the application of the generalized entropy concept in artificial neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512764/ https://www.ncbi.nlm.nih.gov/pubmed/33265339 http://dx.doi.org/10.3390/e20040249 |
work_keys_str_mv | AT gajowniczekkrzysztof simulationstudyontheapplicationofthegeneralizedentropyconceptinartificialneuralnetworks AT orłowskiarkadiusz simulationstudyontheapplicationofthegeneralizedentropyconceptinartificialneuralnetworks AT zabkowskitomasz simulationstudyontheapplicationofthegeneralizedentropyconceptinartificialneuralnetworks |