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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...

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Detalles Bibliográficos
Autores principales: Tang, Qi, Xiao, Jianyu, Wu, Kefeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
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.
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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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