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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.04128 |
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| _version_ | 1866908302799011840 |
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| author | Ma, Chaoxiong Liang, Yan Zhang, Huixia Sun, Hao |
| author_facet | Ma, Chaoxiong Liang, Yan Zhang, Huixia Sun, Hao |
| contents | It is explored that available credible evidence fusion schemes suffer from the potential inconsistency because credibility calculation and Dempster's combination rule-based fusion are sequentially performed in an open-loop style. This paper constructs evidence credibility from the perspective of the degree of support for events within the framework of discrimination (FOD) and proposes an iterative credible evidence fusion (ICEF) to overcome the inconsistency in view of close-loop control. On one hand, the ICEF introduces the fusion result into credibility assessment to establish the correlation between credibility and the fusion result. On the other hand, arithmetic-geometric divergence is promoted based on the exponential normalization of plausibility and belief functions to measure evidence conflict, called plausibility-belief arithmetic-geometric divergence (PBAGD), which is superior in capturing the correlation and difference of FOD subsets, identifying abnormal sources, and reducing their fusion weights. The ICEF is compared with traditional methods by combining different evidence difference measure forms via numerical examples to verify its performance. Simulations on numerical examples and benchmark datasets reflect the adaptability of PBAGD to the proposed fusion strategy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04128 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Guaranteeing consistency in evidence fusion: A novel perspective on credibility Ma, Chaoxiong Liang, Yan Zhang, Huixia Sun, Hao Artificial Intelligence It is explored that available credible evidence fusion schemes suffer from the potential inconsistency because credibility calculation and Dempster's combination rule-based fusion are sequentially performed in an open-loop style. This paper constructs evidence credibility from the perspective of the degree of support for events within the framework of discrimination (FOD) and proposes an iterative credible evidence fusion (ICEF) to overcome the inconsistency in view of close-loop control. On one hand, the ICEF introduces the fusion result into credibility assessment to establish the correlation between credibility and the fusion result. On the other hand, arithmetic-geometric divergence is promoted based on the exponential normalization of plausibility and belief functions to measure evidence conflict, called plausibility-belief arithmetic-geometric divergence (PBAGD), which is superior in capturing the correlation and difference of FOD subsets, identifying abnormal sources, and reducing their fusion weights. The ICEF is compared with traditional methods by combining different evidence difference measure forms via numerical examples to verify its performance. Simulations on numerical examples and benchmark datasets reflect the adaptability of PBAGD to the proposed fusion strategy. |
| title | Guaranteeing consistency in evidence fusion: A novel perspective on credibility |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2504.04128 |