A Novel Evidence Combination Method Based on Improved Pignistic Probability
Evidence theory is widely used to deal with the fusion of uncertain information, but the fusion of conflicting evidence remains an open question. To solve the problem of conflicting evidence fusion in single target recognition, we proposed a novel evidence combination method based on an improved pig...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/6/948 |
| _version_ | 1797594911536578560 |
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| author | Xin Shi Fei Liang Pengjie Qin Liang Yu Gaojie He |
| author_facet | Xin Shi Fei Liang Pengjie Qin Liang Yu Gaojie He |
| author_sort | Xin Shi |
| collection | DOAJ |
| description | Evidence theory is widely used to deal with the fusion of uncertain information, but the fusion of conflicting evidence remains an open question. To solve the problem of conflicting evidence fusion in single target recognition, we proposed a novel evidence combination method based on an improved pignistic probability function. Firstly, the improved pignistic probability function could redistribute the probability of multi-subset proposition according to the weight of single subset propositions in a basic probability assignment (BPA), which reduces the computational complexity and information loss in the conversion process. The combination of the Manhattan distance and evidence angle measurements is proposed to extract evidence certainty and obtain mutual support information between each piece of evidence; then, entropy is used to calculate the uncertainty of the evidence and the weighted average method is used to correct and update the original evidence. Finally, the Dempster combination rule is used to fuse the updated evidence. Verified by the analysis results of single-subset proposition and multi-subset proposition highly conflicting evidence examples, compared to the Jousselme distance method, the Lance distance and reliability entropy combination method, and the Jousselme distance and uncertainty measure combination method, our approach achieved better convergence and the average accuracy was improved by 0.51% and 2.43%. |
| first_indexed | 2024-03-11T02:29:24Z |
| format | Article |
| id | doaj.art-16026daaf8544f3590be019be6c30285 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T02:29:24Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-16026daaf8544f3590be019be6c302852023-11-18T10:18:40ZengMDPI AGEntropy1099-43002023-06-0125694810.3390/e25060948A Novel Evidence Combination Method Based on Improved Pignistic ProbabilityXin Shi0Fei Liang1Pengjie Qin2Liang Yu3Gaojie He4School of Automation, Chongqing University, Chongqing 400044, ChinaSchool of Automation, Chongqing University, Chongqing 400044, ChinaSchool of Automation, Chongqing University, Chongqing 400044, ChinaSchool of Automation, Chongqing University, Chongqing 400044, ChinaSchool of Automation, Chongqing University, Chongqing 400044, ChinaEvidence theory is widely used to deal with the fusion of uncertain information, but the fusion of conflicting evidence remains an open question. To solve the problem of conflicting evidence fusion in single target recognition, we proposed a novel evidence combination method based on an improved pignistic probability function. Firstly, the improved pignistic probability function could redistribute the probability of multi-subset proposition according to the weight of single subset propositions in a basic probability assignment (BPA), which reduces the computational complexity and information loss in the conversion process. The combination of the Manhattan distance and evidence angle measurements is proposed to extract evidence certainty and obtain mutual support information between each piece of evidence; then, entropy is used to calculate the uncertainty of the evidence and the weighted average method is used to correct and update the original evidence. Finally, the Dempster combination rule is used to fuse the updated evidence. Verified by the analysis results of single-subset proposition and multi-subset proposition highly conflicting evidence examples, compared to the Jousselme distance method, the Lance distance and reliability entropy combination method, and the Jousselme distance and uncertainty measure combination method, our approach achieved better convergence and the average accuracy was improved by 0.51% and 2.43%.https://www.mdpi.com/1099-4300/25/6/948DS evidence theorypignistic probability functioninformation fusion |
| spellingShingle | Xin Shi Fei Liang Pengjie Qin Liang Yu Gaojie He A Novel Evidence Combination Method Based on Improved Pignistic Probability Entropy DS evidence theory pignistic probability function information fusion |
| title | A Novel Evidence Combination Method Based on Improved Pignistic Probability |
| title_full | A Novel Evidence Combination Method Based on Improved Pignistic Probability |
| title_fullStr | A Novel Evidence Combination Method Based on Improved Pignistic Probability |
| title_full_unstemmed | A Novel Evidence Combination Method Based on Improved Pignistic Probability |
| title_short | A Novel Evidence Combination Method Based on Improved Pignistic Probability |
| title_sort | novel evidence combination method based on improved pignistic probability |
| topic | DS evidence theory pignistic probability function information fusion |
| url | https://www.mdpi.com/1099-4300/25/6/948 |
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