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Inference rules, decision rules and rough sets
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The article was published on 1999-01-01 and is currently open access. It has received 1 citations till now. The article focuses on the topics: Rough set & Decision rule.read more
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Book ChapterDOI
Decision Rules, Bayes' Rule and Ruogh Sets
TL;DR: This paper concerns a relationship between Bayes’ inference rule and decision rules from the rough set perspective and concludes that Bayes' inference rule should be considered as a guide to decision rules.
References
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Journal ArticleDOI
Fuzzy sets as a basis for a theory of possibility
TL;DR: The theory of possibility described in this paper is related to the theory of fuzzy sets by defining the concept of a possibility distribution as a fuzzy restriction which acts as an elastic constraint on the values that may be assigned to a variable.
Book ChapterDOI
Upper and Lower Probabilities Induced by a Multivalued Mapping
TL;DR: A distinctive feature of the present approach is a rule for conditioning, or more generally, arule for combining sources of information, as discussed in Sects.
Journal ArticleDOI
The role of fuzzy logic in the management of uncertainty in expert systems
TL;DR: F fuzzy logic is suggested, which is the logic underlying approximate or, equivalently, fuzzy reasoning, which leads to various basic syllogisms which may be used as rules of combination of evidence in expert systems.
Journal ArticleDOI
Fuzzy power sets and fuzzy implication operators
Wyllis Bandler,Ladislav Kohout +1 more
TL;DR: The theory of fuzzy power sets is shown very naturally to require the use of a fuzzy implication operator and emphasis is placed on the dependence of the choice of operators upon the purposes the user has in hand.
Journal ArticleDOI
Rough mereology: A new paradigm for approximate reasoning
Lech Polkowski,Andrzej Skowron +1 more
TL;DR: R rough mereology is proposed as a foundation for approximate reasoning about complex objects and approximate proofs understood as schemes constructed to support the authors' assertions about the world on the basis of incomplete or uncertain knowledge.