Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference
Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference
On the use of Hamacher's t-norms family for information aggregation
Information Sciences: an International Journal
Fuzzy Sets and Systems - Special issue: Preference modelling and applications
Assessing sensor reliability for multisensor data fusion within the transferable belief model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information combination operators for data fusion: a comparative review with classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Representing partial ignorance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Modeling of reliability with possibility theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Possibilistic information fusion using maximal coherent subsets
IEEE Transactions on Fuzzy Systems
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Possibility theory offers appealing tools to manage uncertain and imprecise data. This paper studies the problem of fusing information stemming from several sources. Different operators already exist but they have problems with conflicting data. The discounting approach weights the respective impacts of sources and solves most of these problems. But we need to assess the discounting factors correctly. A solution is proposed with the assumption that conflicts come from defective sources. In this paper defective means that we trust a source, and we give it a high reliability, but suddenly it supplies wrong reports that conflict with the reports from other sources. Our algorithm detects such a failure and improves the fusion step. Meanwhile a new fusion rule is introduced. Indeed, the discounting approach extends the support of the resulting distribution to the reference set, which is debatable. A few comparisons are provided.