Abstract
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.
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References
Ayoun, A., Smets, P.: Data association in multi-target detection using the transferable model. Intern. J. Intell. Systems, (to appear) (2001)
Denoeux, T.: A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems, Man, and Cybernetics, Vol 25, N5, May (1995) 804–813
Elouedi, Z., Mellouli, K., Smets, P.: Classification with belief decision trees. the proceedings of the Ninth International Conference on Artificial Intelligence: Methodology, Systems, Applications, AIMSA’2000 (2000) 80–90
Guan, J. W., Bell, D.A.: Discounting and Combination Operations in evidential Reasoning. Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, (1993) 80–90.
Ling, X., Rudd, W.G.: Combining Opinions From Several Experts. Applied Artificial Intelligence 3 (1989), 439–452
Shafer, G.: A mathematical theory of evidence. Princeton University Press (1976)
Shafer, G.: The Combination of Evidence. International Journal of Intelligent Systems 1, (1986) 155–179
Smets, P.: The combination of evidence in the Transferable. Belief Model, IEEE Transactions on Pattern Analysis and Machine Intelligence 12, (1990) 321–344
Smets, P.: The transferable belief model for expert judgments and reliability problems. Analysis and Management of uncertainty: Theory and Applications B.M. Ayyub, M.M. Gupta and L.N. Kanal (editors), Elsevier Science Publishers B.V. (1992)
Smets, P., Kennes, R.: The transferable belief model. Artificial Intelligence 66 (1994) 191–234
Smets, P.: The transferable belief model for quantified belief representation. D.M. Gabbay and Ph. Smets (eds.) Handbook of Defeasible Reasoning and Uncertainty Management Systems 1 Kuwer Doordrecht (1998) 267–301
Smets, P.: The Application of the transferable belief Model to Diagnostic Problems Int. J. Intelligent Systems 13 (1998) 127–158
Smets, P.: Practical Uses of Belief Functions. Laskey K. B. and Prade H. (eds.) Uncertainty in Artificial Intelligence 15 UAI99 (1999) 612–621
Waltz, E., Llinas, J.: Multisensor Data Fusion. Artech House, Boston, (1990)
L. M. Zouhal and T. Denoeux: An evidence-theoretic k-NN rule with parameter optimization. IEEE Transactions on Systems, Man and Cybernetics C, 28(2) (1998) 263–271.
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Elouedi, Z., Mellouli, K., Smets, P. (2001). The Evaluation of Sensors’ Reliability and Their Tuning for Multisensor Data Fusion within the Transferable Belief Model. In: Benferhat, S., Besnard, P. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2001. Lecture Notes in Computer Science(), vol 2143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44652-4_31
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DOI: https://doi.org/10.1007/3-540-44652-4_31
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