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Inference in directed evidential networks based on the transferable belief model.

, and . Int. J. Approx. Reason., 48 (2): 399-418 (2008)

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Naive possibilistic classifiers for imprecise or uncertain numerical data., , , , and . Fuzzy Sets Syst., (2014)Coarsening Approximations of Belief Functions., , and . ECSQARU, volume 2143 of Lecture Notes in Computer Science, page 362-373. Springer, (2001)On Directed and Undirected Propagation Algorithms for Bayesian Networks., , and . ECSQARU, volume 4724 of Lecture Notes in Computer Science, page 598-610. Springer, (2007)Frequent Itemset Mining from Databases Including One Evidential Attribute., , and . SUM, volume 5291 of Lecture Notes in Computer Science, page 19-32. Springer, (2008)From Bayesian Classifiers to Possibilistic Classifiers for Numerical Data., , , and . SUM, volume 6379 of Lecture Notes in Computer Science, page 112-125. Springer, (2010)Assessing sensor reliability for multisensor data fusion within the transferable belief model., , and . IEEE Trans. Syst. Man Cybern. Part B, 34 (1): 782-787 (2004)Inference in directed evidential networks based on the transferable belief model., and . Int. J. Approx. Reason., 48 (2): 399-418 (2008)Using Class-Based Reasoning to Improve the Accuracy of Symbolic Rules in a Hybrid Possibilistic Approach., and . DBKDA, page 222-228. IEEE Computer Society, (2010)A four-decomposition strategies for hierarchically modeling combinatorial optimization problems: framework, conditions and relations., , and . HPCS, page 491-498. IEEE, (2015)Approche binaire pour la génération des fortes règles d'association., , and . EGC, volume RNTI-E-2 of Revue des Nouvelles Technologies de l'Information, page 329-340. Cépaduès-Éditions, (2004)