Abstract
Belief function theory provides a flexible way to combine information provided by different sources. This combination is usually followed by a decision making which can be handled by a range of decision rules. Some rules help to choose the most likely hypothesis. Others allow that a decision is made on a set of hypotheses. In [6], we proposed a decision rule based on a distance measure. First, in this paper, we aim to demonstrate that our proposed decision rule is a particular case of the rule proposed in [4]. Second, we give experiments showing that our rule is able to decide on a set of hypotheses. Some experiments are handled on a set of mass functions generated randomly, others on real databases.
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References
Appriou, A.: Approche générique de la gestion de l’incertain dans les processus de fusion multisenseur. Traitement du Signal 22, 307–319 (2005)
Dempster, A.P.: Upper and Lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics 38, 325–339 (1967)
Denoeux, T.: A k-nearest neighbor classification rule based on Dempster-Shafer Theory. IEEE Transactions on Systems, Man, and Cybernetics 25(5), 804–813 (1995)
Denoeux, T.: Analysis of evidence-theoric decision rules for pattern classification. Pattern Recognition 30(7), 1095–1107 (1997)
Dubois, D., Prade, H.: Representation and combination of uncertainty with belief functions and possibility measures. Computational Intelligence 4, 244–264 (1988)
Essaid, A., Martin, A., Smits, G., Ben Yaghlane, B.: Uncertainty in ontology matching: a decision rule based approach. In: Proceeding of the International Conference on Information Processing and Mangement Uncertainty, pp. 46–55 (2014)
Jousselme, A.L., Grenier, D., Bossé, E.: A New Distance Between Two Bodies of Evidence. Information Fusion 2, 91–101 (2001)
Martin, A., Quidu, I.: Decision support with belief functions theory for seabed characterization. In: Proceeding of the International Conference on Information Fusion, pp. 1–8 (2008)
Shafer, G.: A mathematical theory of evidence. Princeton University Press (1976)
Smets, P.: The Combination of Evidence in the Transferable Belief Model. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(5), 447–458 (1990)
Smets, P.: Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem. International Journal of Approximate Reasoning 9(1), 1–35 (1993)
Smets, P., Kennes, R.: The Transferable Belief Model. Artificial Intelligent 66, 191–234 (1994)
Yager, R.R.: On the Dempster-Shafer framework and new combination rules. Information Sciences 41, 93–137 (1987)
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Essaid, A., Martin, A., Smits, G., Ben Yaghlane, B. (2014). A Distance-Based Decision in the Credal Level. In: Aranda-Corral, G.A., Calmet, J., Martín-Mateos, F.J. (eds) Artificial Intelligence and Symbolic Computation. AISC 2014. Lecture Notes in Computer Science(), vol 8884. Springer, Cham. https://doi.org/10.1007/978-3-319-13770-4_13
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DOI: https://doi.org/10.1007/978-3-319-13770-4_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13769-8
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