Abstract
The distance of evidence, which represents the degree of dissimilarity between bodies of evidence, has attracted more and more interest and has found extensive uses in many realms. In this paper some notes on a widely used distance of evidence, i.e., betting commitment distance, are provided, including the arguments on the rationality of its definition, some misuses and some counter-intuitive behaviors of betting commitment distance. Several numerical examples are also provided to support and verify our arguments.
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References
Shafer G. A Mathematical Theory of Evidence. Princeton: Princeton University Press, 1976
Jousselme A L, Maupin P. On some properties of distances in evidence theory. In: Proceedings of the 1st Workshop on Theory of Belief Functions, Brest, France, 2010. 1–6
Tessem B. Approximations for efficient computation in the theory of evidence. Artif Intel, 1993, 61: 315–329
Fixsen D, Mahler R P S. The modified Dempster-Shafer approach to classification. IEEE Trans Syst Man Cybern A Syst Hum, 1997, 27: 96–104
Jousselme A L, Grenier D, Bosse E. A new distance between two bodies of evidence. Inf Fusion, 2001, 2: 91–101
Guo H W, Shi W K, Deng Y. Evaluating sensor reliability in classification problems based on evidence theory. IEEE Trans Syst Man Cybern B Cybern, 2006, 36: 970–981
Deng Y, Shi W K, Zhu Z F, et al. Combining belief functions based on distance of evidence. Decis Support Syst, 2004, 38: 489–493
Liu W R. Analyzing the degree of conflict among belief functions. Artif Intel, 2006, 170: 909–924
Ristic B, Smets P. The TBM global distance measure for the association of uncertain combat ID declarations. Inf Fusion, 2006, 7: 276–284
Ristic B, Smets P. Global cost of assignment in the TBM framework for association of uncertain ID reports. Aerosp Sci Technol, 2007, 11: 303–309
Ben-Hariz S, Elouedi Z, Mellouli K. Clustering approach using belief function theory. Lect Notes Comput Sci, 2006, 4183: 162–171
Wen C L, Wang Y, Xu X B. Fuzzy information fusion algorithm of fault diagnosis based on similarity measure of evidence. Lect Notes Comput Sci, 2008, 5264: 506–515
Zouhal L M, Denoeux T. An evidence-theoretic k-NN rule with parameter optimization. IEEE Trans Syst Man Cybern C, 1998, 28: 263–271
Smets P, Kennes R. The transferable belief model. Artif Intel, 1994, 66: 191–234
Bauer M. Approximation algorithms and decision making in the Dempster-Shafer theory of evidence-an empirical study. Int J Approx Reason, 1997, 17: 217–237
Cuzzolin F. A geometric approach to the theory of evidence. IEEE Trans Syst Man Cybern C Appl Rev, 2008, 38: 522–534
Jousselme A L, Liu C S, Grenier D, et al. Measuring ambiguity in the evidence theory. IEEE Trans Syst Man Cybern A, 2006, 36: 890–903
Florea M C, Bosse E. Crisis management using Dempster-Shafer theory: using dissimilarity measures to characterize sources’ reliability. In: C3I in Crisis, Emergency and Consequence Management, RTO-MP-IST-086, Bucharest, Romania, 2009
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Han, D., Deng, Y., Han, C. et al. Some notes on betting commitment distance in evidence theory. Sci. China Inf. Sci. 55, 558–565 (2012). https://doi.org/10.1007/s11432-011-4541-z
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DOI: https://doi.org/10.1007/s11432-011-4541-z