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Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion
Dempster-Shafer (DS) evidence theory is widely used in various fields of uncertain information processing, but it may produce counterintuitive results when dealing with conflicting data. Therefore, this paper proposes a new data fusion method which combines the Deng entropy and the negation of basic...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8066141/ https://www.ncbi.nlm.nih.gov/pubmed/33800628 http://dx.doi.org/10.3390/e23040402 |
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author | Chen, Yutong Tang, Yongchuan |
author_facet | Chen, Yutong Tang, Yongchuan |
author_sort | Chen, Yutong |
collection | PubMed |
description | Dempster-Shafer (DS) evidence theory is widely used in various fields of uncertain information processing, but it may produce counterintuitive results when dealing with conflicting data. Therefore, this paper proposes a new data fusion method which combines the Deng entropy and the negation of basic probability assignment (BPA). In this method, the uncertain degree in the original BPA and the negation of BPA are considered simultaneously. The degree of uncertainty of BPA and negation of BPA is measured by the Deng entropy, and the two uncertain measurement results are integrated as the final uncertainty degree of the evidence. This new method can not only deal with the data fusion of conflicting evidence, but it can also obtain more uncertain information through the negation of BPA, which is of great help to improve the accuracy of information processing and to reduce the loss of information. We apply it to numerical examples and fault diagnosis experiments to verify the effectiveness and superiority of the method. In addition, some open issues existing in current work, such as the limitations of the Dempster-Shafer theory (DST) under the open world assumption and the necessary properties of uncertainty measurement methods, are also discussed in this paper. |
format | Online Article Text |
id | pubmed-8066141 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80661412021-04-25 Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion Chen, Yutong Tang, Yongchuan Entropy (Basel) Article Dempster-Shafer (DS) evidence theory is widely used in various fields of uncertain information processing, but it may produce counterintuitive results when dealing with conflicting data. Therefore, this paper proposes a new data fusion method which combines the Deng entropy and the negation of basic probability assignment (BPA). In this method, the uncertain degree in the original BPA and the negation of BPA are considered simultaneously. The degree of uncertainty of BPA and negation of BPA is measured by the Deng entropy, and the two uncertain measurement results are integrated as the final uncertainty degree of the evidence. This new method can not only deal with the data fusion of conflicting evidence, but it can also obtain more uncertain information through the negation of BPA, which is of great help to improve the accuracy of information processing and to reduce the loss of information. We apply it to numerical examples and fault diagnosis experiments to verify the effectiveness and superiority of the method. In addition, some open issues existing in current work, such as the limitations of the Dempster-Shafer theory (DST) under the open world assumption and the necessary properties of uncertainty measurement methods, are also discussed in this paper. MDPI 2021-03-28 /pmc/articles/PMC8066141/ /pubmed/33800628 http://dx.doi.org/10.3390/e23040402 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Chen, Yutong Tang, Yongchuan Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion |
title | Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion |
title_full | Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion |
title_fullStr | Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion |
title_full_unstemmed | Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion |
title_short | Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion |
title_sort | measuring the uncertainty in the original and negation of evidence using belief entropy for conflict data fusion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8066141/ https://www.ncbi.nlm.nih.gov/pubmed/33800628 http://dx.doi.org/10.3390/e23040402 |
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