Learning and decision-making in the framework of fuzzy lattices
New learning paradigms in soft computing
Risk assessment system of natural hazards: A new approach based on fuzzy probability
Fuzzy Sets and Systems
Colour image segmentation using homogeneity method and data fusion techniques
EURASIP Journal on Advances in Signal Processing - Image processing and analysis in biomechanics
On generalized fuzzy belief functions in infinite spaces
IEEE Transactions on Fuzzy Systems
Semigroup structure of singleton Dempster-Shafer evidence accumulation
IEEE Transactions on Information Theory
The geometry of consonant belief functions: Simplicial complexes of necessity measures
Fuzzy Sets and Systems
Combining uncertainty and imprecision in models of medical diagnosis
Information Sciences: an International Journal
On Some Mathematical Structures of T-Fuzzy Rough Set Algebras in Infinite Universes of Discourse
Fundamenta Informaticae - Advances in Rough Set Theory
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The Dempster-Shafer theory (DST) may be considered as a generalization of the probability theory, which assigns mass values to the subsets of the referential set and suggests an interval-valued probability measure. There have been several attempts for fuzzy generalization of the DST by assigning mass (probability) values to the fuzzy subsets of the referential set. The interval-valued probability measures thus obtained are not equivalent to the original fuzzy body of evidence. In this paper, a new generalization of the DST is put forward that gives a fuzzy-valued definition for the belief, plausibility, and probability functions over a finite referential set. These functions are all equivalent to one another and to the original fuzzy body of evidence. The advantage of the proposed model is shown in three application examples. It can be seen that the proposed generalization is capable of modeling the uncertainties in the real world and eliminate the need for extra preassumptions and preprocessing