Detection of defective sources in the setting of possibility theory
Fuzzy Sets and Systems
Issues in data fusion for healthcare monitoring
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
Incomplete Statistical Information Fusion and Its Application to Clinical Trials Data
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
Correspondence: Comments on “A new combination of evidence based on compromise” by K. Yamada
Fuzzy Sets and Systems
Structured and real time heterogeneous sensor deployment in preferential areas
Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
Dynamic Reduct from Partially Uncertain Data Using Rough Sets
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
In Memoriam: Philippe Smets (1938--2005)
Fuzzy Sets and Systems
In memoriam: Philippe Smets (1938-2005)
International Journal of Approximate Reasoning
Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion
Engineering Applications of Artificial Intelligence
A comparison of dynamic and static belief rough set classifier
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Rule discovery process based on rough sets under the belief function framework
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Combining neural networks based on Dempster-Shafer theory for classifying data with imperfect labels
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Classifier fusion in the Dempster--Shafer framework using optimized t-norm based combination rules
International Journal of Approximate Reasoning
Classification with dynamic reducts and belief functions
Transactions on rough sets XIV
Towards an alarm for opposition conflict in a conjunctive combination of belief functions
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Expert Systems with Applications: An International Journal
Classification systems based on rough sets under the belief function framework
International Journal of Approximate Reasoning
Belief functions contextual discounting and canonical decompositions
International Journal of Approximate Reasoning
Relevance and truthfulness in information correction and fusion
International Journal of Approximate Reasoning
Clustering approach using belief function theory
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
Contextual discounting of belief functions
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Towards a definition of evaluation criteria for probabilistic classifiers
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Heuristic for attribute selection using belief discernibility matrix
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Multisensor data fusion: A review of the state-of-the-art
Information Fusion
A belief function distance metric for orderable sets
Information Fusion
How to preserve the conflict as an alarm in the combination of belief functions?
Decision Support Systems
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This paper presents a method for assessing the reliability of a sensor in a classification problem based on the transferable belief model. First, we develop a method for the evaluation of the reliability of a sensor when considered alone. The method is based on finding the discounting factor minimizing the distance between the pignistic probabilities computed from the discounted beliefs and the actual values of data. Next, we develop a method for assessing the reliability of several sensors that are supposed to work jointly and their readings are aggregated. The discounting factors are computed on the basis of minimizing the distance between the pignistic probabilities computed from the combined discounted belief functions and the actual values of data.