The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining opinions from several experts
Applied Artificial Intelligence
Artificial Intelligence
Multisensor Data Fusion
Classification with Belief Decision Trees
AIMSA '00 Proceedings of the 9th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
Practical uses of belief functions
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
An evidence-theoretic k-NN rule with parameter optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Expert Systems with Applications: An International Journal
Selecting source behavior in information fusion on the basis of consistency and specificity
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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We develop a method to evaluate the reliability of a sensor in a classification task when the uncertainty is represented by belief functions as understood in the transferable belief model. This reliability is represented by a discounting factor that minimizes the distance between the pignistic probabilities computed from the discounted beliefs and the actual values of the data in a learning set. We then describe a method to tune the discounting factors of several sensors when their reports are merged in order to reach an aggregated report. They are computed so that together they minimize the distance between the pignistic probabilities computed from the combined discounted belief functions and the actual values of the data in a learning set. The first method produces the reliability of a sensor considered alone. The second method considers a set of sensors, and weights each of them so that together they produce the best predictor.