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Complex Evidence Theory for Multisource Data Fusion
Review Article  ·  Published: 30 September 2024
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Chinese Journal of Information Fusion
Volume 1, Issue 2, 2024: 134-159
Review Article Feature Paper Open Access

Complex Evidence Theory for Multisource Data Fusion

by
Fuyuan Xiao Fuyuan Xiao 1 * ORCID
Junhao Wen Junhao Wen 1 ORCID
Witold Pedrycz Witold Pedrycz 2,3,4 ORCID
Masayoshi Aritsugi Masayoshi Aritsugi 5 ORCID
1 School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China
2 Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
3 Systems Research Institute, Polish Academy of Sciences, 00-901 Warsaw, Poland
4 National Information Processing Institute, 00-608 Warsaw, Poland
5 Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto 860-8555, Japan
* Corresponding Author: Fuyuan Xiao, [email protected]
Volume 1, Issue 2
Volume 1, Issue 2 2024
Received: 30 July 2024 Accepted: 28 September 2024 Published: 30 September 2024 CrossMark
Download PDF 1.49 MB HTML XML Cite Metrics
DOI: 10.62762/CJIF.2024.999646
ARK: ark:/57805/cjif.2024.999646

Article Information

Published in Chinese Journal of Information Fusion
Volume/Issue Volume 1, Issue 2, 2024
Pages 134-159
Cited by 27 (Crossref) 29 (Scopus)

Abstract

Data fusion is a prevalent technique for assembling imperfect raw data coming from multiple sources to capture reliable and accurate information. Dempster–Shafer evidence theory is one of useful methodologies in the fusion of uncertain multisource information. The existing literature lacks a thorough and comprehensive review of the recent advances of Dempster– Shafer evidence theory for data fusion. Therefore, the state of the art has to be surveyed to gain insight into how Dempster–Shafer evidence theory is beneficial for data fusion and how it evolved over time. In this paper, we first provide a comprehensive review of data fusion methods based on Dempster–Shafer evidence theory and its extensions, collectively referred to as classical evidence theory, from three aspects of uncertainty modeling, fusion, and decision making. Next, we study and explore complex evidence theory for data fusion in both closed world and open world contexts that benefits from the frame of complex plane modelling. We then present classical and complex evidence theory framework-based multisource data fusion algorithms, which are applied to pattern classification to compare and demonstrate their applicabilities. The research results indicate that the complex evidence theory framework can enhance the capabilities of uncertainty modeling and reasoning by generating constructive interference through the fusion of appropriate complex basic belief assignment functions modeled by complex numbers. Through analysis and comparison, we finally propose several challenges and identify open future research directions in evidence theorybased data fusion.

Graphical Abstract

Complex Evidence Theory for Multisource Data Fusion

Keywords

multisource data fusion Dempster-Shafer evidence theory complex evidence theory quantum evidence theory uncertainty modeling conflict management belief function decision making pattern classification

Data Availability Statement

Data will be made available on request.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62473067; in part by the Chongqing Talents: Exceptional Young Talents Project under Grant cstc2022ycjh-bgzxm0070; in part by the Chongqing Overseas Scholars Innovation Program under Grant cx2022024.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

Not applicable.

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APA Style
Xiao, F., Wen, J., Pedrycz, W., & Aritsugi, M. (2024). Complex Evidence Theory for Multisource Data Fusion. Chinese Journal of Information Fusion, 1(2), 134–159. https://doi.org/10.62762/CJIF.2024.999646
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TY  - JOUR
AU  - Xiao, Fuyuan
AU  - Wen, Junhao
AU  - Pedrycz, Witold
AU  - Aritsugi, Masayoshi
PY  - 2024
DA  - 2024/09/30
TI  - Complex Evidence Theory for Multisource Data Fusion
JO  - Chinese Journal of Information Fusion
T2  - Chinese Journal of Information Fusion
JF  - Chinese Journal of Information Fusion
VL  - 1
IS  - 2
SP  - 134
EP  - 159
DO  - 10.62762/CJIF.2024.999646
UR  - https://www.icck.org/article/abs/CJIF.2024.999646
KW  - multisource data fusion
KW  - Dempster-Shafer evidence theory
KW  - complex evidence theory
KW  - quantum evidence theory
KW  - uncertainty modeling
KW  - conflict management
KW  - belief function
KW  - decision making
KW  - pattern classification
AB  - Data fusion is a prevalent technique for assembling imperfect raw data coming from multiple sources to capture reliable and accurate information. Dempster–Shafer evidence theory is one of useful methodologies in the fusion of uncertain multisource information. The existing literature lacks a thorough and comprehensive review of the recent advances of Dempster– Shafer evidence theory for data fusion. Therefore, the state of the art has to be surveyed to gain insight into how Dempster–Shafer evidence theory is beneficial for data fusion and how it evolved over time. In this paper, we first provide a comprehensive review of data fusion methods based on Dempster–Shafer evidence theory and its extensions, collectively referred to as classical evidence theory, from three aspects of uncertainty modeling, fusion, and decision making. Next, we study and explore complex evidence theory for data fusion in both closed world and open world contexts that benefits from the frame of complex plane modelling. We then present classical and complex evidence theory framework-based multisource data fusion algorithms, which are applied to pattern classification to compare and demonstrate their applicabilities. The research results indicate that the complex evidence theory framework can enhance the capabilities of uncertainty modeling and reasoning by generating constructive interference through the fusion of appropriate complex basic belief assignment functions modeled by complex numbers. Through analysis and comparison, we finally propose several challenges and identify open future research directions in evidence theorybased data fusion.
SN  - 2998-3371
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Xiao2024Complex,
  author = {Fuyuan Xiao and Junhao Wen and Witold Pedrycz and Masayoshi Aritsugi},
  title = {Complex Evidence Theory for Multisource Data Fusion},
  journal = {Chinese Journal of Information Fusion},
  year = {2024},
  volume = {1},
  number = {2},
  pages = {134-159},
  doi = {10.62762/CJIF.2024.999646},
  url = {https://www.icck.org/article/abs/CJIF.2024.999646},
  abstract = {Data fusion is a prevalent technique for assembling imperfect raw data coming from multiple sources to capture reliable and accurate information. Dempster–Shafer evidence theory is one of useful methodologies in the fusion of uncertain multisource information. The existing literature lacks a thorough and comprehensive review of the recent advances of Dempster– Shafer evidence theory for data fusion. Therefore, the state of the art has to be surveyed to gain insight into how Dempster–Shafer evidence theory is beneficial for data fusion and how it evolved over time. In this paper, we first provide a comprehensive review of data fusion methods based on Dempster–Shafer evidence theory and its extensions, collectively referred to as classical evidence theory, from three aspects of uncertainty modeling, fusion, and decision making. Next, we study and explore complex evidence theory for data fusion in both closed world and open world contexts that benefits from the frame of complex plane modelling. We then present classical and complex evidence theory framework-based multisource data fusion algorithms, which are applied to pattern classification to compare and demonstrate their applicabilities. The research results indicate that the complex evidence theory framework can enhance the capabilities of uncertainty modeling and reasoning by generating constructive interference through the fusion of appropriate complex basic belief assignment functions modeled by complex numbers. Through analysis and comparison, we finally propose several challenges and identify open future research directions in evidence theorybased data fusion.},
  keywords = {multisource data fusion, Dempster-Shafer evidence theory, complex evidence theory, quantum evidence theory, uncertainty modeling, conflict management, belief function, decision making, pattern classification},
  issn = {2998-3371},
  publisher = {Institute of Central Computation and Knowledge}
}

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CC BY Copyright © 2024 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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North University of China, China

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