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Counter Deception in Belief Functions Using Shapley Value Methodology
- Source :
- International Journal of Fuzzy Systems. 24:340-354
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- Counter deception is one of the main content in data fusion. The existence of deceptive data may cause great hidden dangers to the generation of correct decisions. While among previous studies, whether evidence should aggregate is still virgin and may become a fascinating question. In this paper, a new counter deception model based on the Shapley value methodology is proposed, which provides a perspective for determining the weight of evidence. Then, we present that the distance of evidence is a kind of “marginal contribution” to the anomaly of the entire fusion system. Moreover, we also investigated the properties of the proposed method to judge whether there is deceptive data in the information fusion based on the cooperation benefits of all basic belief assignment (BBA) combinations. Several numerical examples and a classification application were used to illustrate the practicability and effectiveness of the proposed methodology.
- Subjects :
- Computer science
Basic belief
business.industry
media_common.quotation_subject
Perspective (graphical)
Aggregate (data warehouse)
Computational intelligence
Deception
Sensor fusion
Shapley value
Theoretical Computer Science
Information fusion
Computational Theory and Mathematics
Artificial Intelligence
Artificial intelligence
business
Software
media_common
Subjects
Details
- ISSN :
- 21993211 and 15622479
- Volume :
- 24
- Database :
- OpenAIRE
- Journal :
- International Journal of Fuzzy Systems
- Accession number :
- edsair.doi...........dfee9a692c45a6a94bf32d9a866df965
- Full Text :
- https://doi.org/10.1007/s40815-021-01139-1