Boosting complex evidence theory with complex belief Renyi divergence for multi-source information fusion
Abstract Complex evidence theory (CET) plays a critical role in addressing uncertainty within the complex domain. However, accurately measuring conflicts between complex mass functions (CMFs) remains a challenge. To solve this issue, we propose the symmetric complex Renyi (SCR) divergence, which ext...
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44443-025-00084-5 |
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| _version_ | 1849389091856056320 |
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| author | Yuhao Qin Jinguo Zhang Zhike Qiu Zichong Chen Rui Cai |
| author_facet | Yuhao Qin Jinguo Zhang Zhike Qiu Zichong Chen Rui Cai |
| author_sort | Yuhao Qin |
| collection | DOAJ |
| description | Abstract Complex evidence theory (CET) plays a critical role in addressing uncertainty within the complex domain. However, accurately measuring conflicts between complex mass functions (CMFs) remains a challenge. To solve this issue, we propose the symmetric complex Renyi (SCR) divergence, which extends the traditional Renyi divergence into the complex domain by incorporating both magnitude and phase information. SCR divergence satisfies the essential properties of symmetry, non-negativity, and non-degeneracy, making it a reliable tool for conflict quantification in uncertain environments. Based on SCR divergence, we develop a novel multi-source information fusion algorithm that dynamically adjusts evidence weights according to conflict levels, effectively mitigating inconsistencies and improving fusion outcomes. Numerical experiments validate the efficiency and robustness of the proposed method, demonstrating its advantages over traditional approaches. Furthermore, the proposed method is applied in medical diagnosis and target recognition, showcasing its practicality and effectiveness in real-world decision-making scenarios. These results highlight the potential of the SCR divergence and the fusion algorithm to address conflict resolution and information integration challenges in complex systems. |
| format | Article |
| id | doaj-art-edcc98f5debb415b86e74aa25fbb50d1 |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-edcc98f5debb415b86e74aa25fbb50d12025-08-20T03:42:03ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-06-0137412310.1007/s44443-025-00084-5Boosting complex evidence theory with complex belief Renyi divergence for multi-source information fusionYuhao Qin0Jinguo Zhang1Zhike Qiu2Zichong Chen3Rui Cai4Business College, Southwest UniversityBusiness College, Southwest UniversityBusiness College, Southwest UniversityBusiness College, Southwest UniversityBusiness College, Southwest UniversityAbstract Complex evidence theory (CET) plays a critical role in addressing uncertainty within the complex domain. However, accurately measuring conflicts between complex mass functions (CMFs) remains a challenge. To solve this issue, we propose the symmetric complex Renyi (SCR) divergence, which extends the traditional Renyi divergence into the complex domain by incorporating both magnitude and phase information. SCR divergence satisfies the essential properties of symmetry, non-negativity, and non-degeneracy, making it a reliable tool for conflict quantification in uncertain environments. Based on SCR divergence, we develop a novel multi-source information fusion algorithm that dynamically adjusts evidence weights according to conflict levels, effectively mitigating inconsistencies and improving fusion outcomes. Numerical experiments validate the efficiency and robustness of the proposed method, demonstrating its advantages over traditional approaches. Furthermore, the proposed method is applied in medical diagnosis and target recognition, showcasing its practicality and effectiveness in real-world decision-making scenarios. These results highlight the potential of the SCR divergence and the fusion algorithm to address conflict resolution and information integration challenges in complex systems.https://doi.org/10.1007/s44443-025-00084-5Complex evidence theorySymmetric complex Renyi divergenceComplex mass functionsMulti-source information fusion |
| spellingShingle | Yuhao Qin Jinguo Zhang Zhike Qiu Zichong Chen Rui Cai Boosting complex evidence theory with complex belief Renyi divergence for multi-source information fusion Journal of King Saud University: Computer and Information Sciences Complex evidence theory Symmetric complex Renyi divergence Complex mass functions Multi-source information fusion |
| title | Boosting complex evidence theory with complex belief Renyi divergence for multi-source information fusion |
| title_full | Boosting complex evidence theory with complex belief Renyi divergence for multi-source information fusion |
| title_fullStr | Boosting complex evidence theory with complex belief Renyi divergence for multi-source information fusion |
| title_full_unstemmed | Boosting complex evidence theory with complex belief Renyi divergence for multi-source information fusion |
| title_short | Boosting complex evidence theory with complex belief Renyi divergence for multi-source information fusion |
| title_sort | boosting complex evidence theory with complex belief renyi divergence for multi source information fusion |
| topic | Complex evidence theory Symmetric complex Renyi divergence Complex mass functions Multi-source information fusion |
| url | https://doi.org/10.1007/s44443-025-00084-5 |
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