Titlebook: Belief Functions: Theory and Applications; 6th International Co Thierry Denœux,Eric Lefèvre,Frédéric Pichon Conference proceedings 2021 Spr

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https://doi.org/10.1007/978-1-349-86189-7a-cluster to the associated new classes using the .-Nearest neighbor technique. Two experiments show that CClu can handle imbalanced datasets with high accuracy, and the errors are reduced by properly modeling imprecision.

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Unequal Singleton Pair Distance for Evidential Preference Clusteringy and imprecision. However, traditional distances on belief functions do not adapt to some intrinsic properties of preference relations, especially when indifference relation is taken into comparison, therefore may cause inconsistent results in preference-based applications. In order to solve this i

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Transfer Evidential C-Means Clusteringa powerful clustering algorithm developed in the theoretical framework of belief functions. Based on the concept of credal partition, it extends those of hard, fuzzy, and possibilistic clustering algorithms. However, as a clustering algorithm, it can only work well when the data is sufficient and th

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https://doi.org/10.1007/978-3-030-88601-1artificial intelligence; combination rules; computer hardware; computer science; computer systems; comput