On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
Artificial Intelligence
Toward a logic of perceptions based on fuzzy logic
Discovering the world with fuzzy logic
Data mining, rough sets and granular computing
Data mining, rough sets and granular computing
Toward a generalized theory of uncertainty (GTU): an outline
Information Sciences—Informatics and Computer Science: An International Journal
Journal of the American Society for Information Science and Technology
Discussion: From imprecise to granular probabilities
Fuzzy Sets and Systems
Classic Works of the Dempster-Shafer Theory of Belief Functions
Classic Works of the Dempster-Shafer Theory of Belief Functions
Mass function derivation and combination in multivariate data spaces
Information Sciences: an International Journal
Reasoning with doubly uncertain soft constraints
International Journal of Approximate Reasoning
Combination rule of D-S evidence theory based on the strategy of cross merging between evidences
Expert Systems with Applications: An International Journal
Fuzzy aggregation operators in decision making with Dempster-Shafer belief structure
Expert Systems with Applications: An International Journal
The conjunctive combination of interval-valued belief structures from dependent sources
International Journal of Approximate Reasoning
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We investigate the problem of inferring information about the value of a variable V from its relationship with another variable U and information about U. We consider two approaches, one using the fuzzy set based theory of approximate reasoning and the other using probabilistic reasoning. Both of these approaches allow the inclusion of imprecise granular type information. The inferred values from each of these methods are then represented using a Dempster-Shafer belief structure. We then compare these values and show an underling unity between these two approaches.