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A novel evidence combination method based on stochastic approach for link-structure analysis algorithm and Lance-Williams distance
In response to the traditional Dempster–Shafer (D-S) combination rule that cannot handle highly conflicting evidence, an evidence combination method based on the stochastic approach for link-structure analysis (SALSA) algorithm combined with Lance-Williams distance is proposed. Firstly, the degree o...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280417/ https://www.ncbi.nlm.nih.gov/pubmed/37346698 http://dx.doi.org/10.7717/peerj-cs.1307 |
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author | Tang, Qi Xiao, Jianyu Wu, Kefeng |
author_facet | Tang, Qi Xiao, Jianyu Wu, Kefeng |
author_sort | Tang, Qi |
collection | PubMed |
description | In response to the traditional Dempster–Shafer (D-S) combination rule that cannot handle highly conflicting evidence, an evidence combination method based on the stochastic approach for link-structure analysis (SALSA) algorithm combined with Lance-Williams distance is proposed. Firstly, the degree of conflict between evidences is calculated based on the number of correlation coefficients between evidences. Then, the evidences with a number of correlation coefficients greater than the average number of correlation coefficients of evidence are connected to construct an evidence association network. The authority weight of the evidence is calculated based on the number of citations in the concept of SALSA algorithm combined with the support of the evidence. Subsequently, the Lance-Williams distance between the evidences is calculated and transformed into support of the evidence. Next, the authority weight and support of evidence are combined to jointly construct a novel correction coefficient to correct the evidence. Finally, the corrected evidence is fused using the D-S combination rule to obtain the final fusion result. The numerical results verify that the method proposed in this paper can effectively solve the problem of the traditional D-S combination rule being unable to handle highly conflicting evidence. |
format | Online Article Text |
id | pubmed-10280417 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102804172023-06-21 A novel evidence combination method based on stochastic approach for link-structure analysis algorithm and Lance-Williams distance Tang, Qi Xiao, Jianyu Wu, Kefeng PeerJ Comput Sci Algorithms and Analysis of Algorithms In response to the traditional Dempster–Shafer (D-S) combination rule that cannot handle highly conflicting evidence, an evidence combination method based on the stochastic approach for link-structure analysis (SALSA) algorithm combined with Lance-Williams distance is proposed. Firstly, the degree of conflict between evidences is calculated based on the number of correlation coefficients between evidences. Then, the evidences with a number of correlation coefficients greater than the average number of correlation coefficients of evidence are connected to construct an evidence association network. The authority weight of the evidence is calculated based on the number of citations in the concept of SALSA algorithm combined with the support of the evidence. Subsequently, the Lance-Williams distance between the evidences is calculated and transformed into support of the evidence. Next, the authority weight and support of evidence are combined to jointly construct a novel correction coefficient to correct the evidence. Finally, the corrected evidence is fused using the D-S combination rule to obtain the final fusion result. The numerical results verify that the method proposed in this paper can effectively solve the problem of the traditional D-S combination rule being unable to handle highly conflicting evidence. PeerJ Inc. 2023-04-18 /pmc/articles/PMC10280417/ /pubmed/37346698 http://dx.doi.org/10.7717/peerj-cs.1307 Text en ©2023 Tang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Algorithms and Analysis of Algorithms Tang, Qi Xiao, Jianyu Wu, Kefeng A novel evidence combination method based on stochastic approach for link-structure analysis algorithm and Lance-Williams distance |
title | A novel evidence combination method based on stochastic approach for link-structure analysis algorithm and Lance-Williams distance |
title_full | A novel evidence combination method based on stochastic approach for link-structure analysis algorithm and Lance-Williams distance |
title_fullStr | A novel evidence combination method based on stochastic approach for link-structure analysis algorithm and Lance-Williams distance |
title_full_unstemmed | A novel evidence combination method based on stochastic approach for link-structure analysis algorithm and Lance-Williams distance |
title_short | A novel evidence combination method based on stochastic approach for link-structure analysis algorithm and Lance-Williams distance |
title_sort | novel evidence combination method based on stochastic approach for link-structure analysis algorithm and lance-williams distance |
topic | Algorithms and Analysis of Algorithms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280417/ https://www.ncbi.nlm.nih.gov/pubmed/37346698 http://dx.doi.org/10.7717/peerj-cs.1307 |
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