Abstract
In the theory of belief functions, the disagreement between sources is often measured in terms of conflict or dissimilarity. These measures are global to the sources, and provide few information about the origin of the disagreement. We propose in this paper a “finer” measure based on the decomposition of the global measure of conflict (or distance). It allows focusing the measure on some hypotheses of interest (namely the ones likely to be chosen after fusion).We apply the proposed so called “local” measures of conflict and distance to the choice of sources for vehicle localization.We show that considering sources agreement/disagreement outperforms blind fusion.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bak, A., Bouchafa, S., Aubert, D.: Detection of independently moving objects through stereo vision and ego-motion extraction. In: Intelligent Vehicles Symposium (IV), pp. 863–870. IEEE (2010)
Denoeux, T.: Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence. Artificial Intelligence 172(2-3), 234–264 (2008)
Dubois, D., Prade, H.: Representation and combination of uncertainty with belief functions and possibility measures. Computational Intelligence 4(3), 244–264 (1988)
Jousselme, A.L., Maupin, P.: Distances in evidence theory: Comprehensive survey and generalizations. International Journal of Approximate Reasoning 53(2), 118–145 (2012)
Liu, W.: Analyzing the degree of conflict among belief functions. Artificial Intelligence 170(11), 909–924 (2006)
Martin, A., Jousselme, A.L., Osswald, C.: Conflict measure for the discounting operation on belief functions. In: The 11th Annual Conference on Information Fusion, pp. 1–8. IEEE, Cologne, Germany (2008)
Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: A factored solution to the simultaneous localization and mapping problem. In: National Conference on Artificial Intelligence, pp. 593–598. AAAI, Menlo Park (2002)
Schubert, J.: Conflict management in Dempster-Shafer theory using the degree of falsity. International Journal of Approximate Reasoning 52(3), 449–460 (2011)
Smets, P.: The canonical decomposition of a weighted belief. In: 14th International Joint Conference on Artificial intelligence, pp. 1896–1901. Morgan Kaufmann Publishers Inc., San Francisco (1995)
Smets, P.: Analyzing the combination of conflicting belief functions. Information Fusion 8(4), 387–412 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Roquel, A., Le Hégarat-Mascle, S., Bloch, I., Vincke, B. (2012). A New Local Measure of Disagreement between Belief Functions – Application to Localization. In: Denoeux, T., Masson, MH. (eds) Belief Functions: Theory and Applications. Advances in Intelligent and Soft Computing, vol 164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29461-7_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-29461-7_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29460-0
Online ISBN: 978-3-642-29461-7
eBook Packages: EngineeringEngineering (R0)