Name
Abstract | ||
|---|---|---|
We introduce the fuzzy measure and discuss its use as a unifying structure for modeling knowledge about an uncertain variable. We show that a large class of well-established types of uncertainty representations can be modeled within this framework. A view of the Dempster-Shafer (D-S) belief structure as an uncertainty representation corresponding to a set of possible fuzzy measures is discussed. A methodology for generating this set of fuzzy measures from a belief structure is described. A measure of entropy associated with a fuzzy measure is introduced and its manifestation for different fuzzy measures is described. The problem of uncertain decision making for the case in which the uncertainty represented by a fuzzy measure is considered. The Choquet integral is introduced as providing a generalization of the expected value to this environment. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/3477.979955 | IEEE Transactions on Systems, Man, and Cybernetics, Part B |
Keywords | Field | DocType |
belief structure,different fuzzy measure,expected value,uncertainty representation,uncertain decision,large class,fuzzy measure,uncertain variable,unifying structure,possible fuzzy measure,set theory,decision theory,dempster shafer,helium,fuzzy set theory,fuzzy sets,rough sets,integral equations,measurement uncertainty,choquet integral,knowledge representation,possibility theory,intelligent systems,entropy | Mathematical optimization,Fuzzy classification,Defuzzification,Fuzzy set operations,Computer science,Fuzzy measure theory,Fuzzy mathematics,Artificial intelligence,Type-2 fuzzy sets and systems,Fuzzy number,Membership function,Machine learning | Journal |
Volume | Issue | ISSN |
32 | 1 | 1083-4419 |
Citations | PageRank | References |
30 | 3.88 | 5 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ronald R. Yager | 1 | 986 | 206.03 |