Further remarks on the relation between rough and fuzzy sets
Fuzzy Sets and Systems
Constructive and algebraic methods of the theory of rough sets
Information Sciences: an International Journal
A comparative study of fuzzy sets and rough sets
Information Sciences: an International Journal
Axiomatics for fuzzy rough sets
Fuzzy Sets and Systems
On the logic foundation of fuzzy reasoning
Information Sciences: an International Journal
A modified genetic algorithm for single machine scheduling
Computers and Industrial Engineering
Granulation of a fuzzy set: Nonspecificity
Information Sciences: an International Journal
(θ,T)-fuzzy rough approximation operators and the TL-fuzzy rough ideals on a ring
Information Sciences: an International Journal
Rough set theory for the interval-valued fuzzy information systems
Information Sciences: an International Journal
Computers & Mathematics with Applications
An interval type-2 fuzzy rough set model for attribute reduction
IEEE Transactions on Fuzzy Systems
FRSVMs: Fuzzy rough set based support vector machines
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
A feedback based CRI approach to fuzzy reasoning
Applied Soft Computing
Rough-set-based association rules applied to brand trust evaluation model
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
The ϑ-lower and T-upper fuzzy rough approximation operators on a semigroup
Information Sciences: an International Journal
A fuzzy rule-based approach for screening international distribution centres
Computers & Mathematics with Applications
Fuzzy rough DEA model: A possibility and expected value approaches
Expert Systems with Applications: An International Journal
A novel variable precision (θ,σ)-fuzzy rough set model based on fuzzy granules
Fuzzy Sets and Systems
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In this paper, we study the fuzzy reasoning based on a new fuzzy rough set. First, we define a broad family of new lower and upper approximation operators of fuzzy sets between different universes using a set of axioms. Then, based on the approximation operators above, we propose the fuzzy reasoning based on the new fuzzy rough set. By means of the above fuzzy reasoning based on the new fuzzy rough set, for a given premise, we can obtain the fuzzy reasoning consequence expressed by the fuzzy interval constructed by the above two approximations of fuzzy sets. Furthermore, through the defuzzification of the lower and upper approximations, we can get the corresponding two values constructing the interval used as the fuzzy reasoning consequence after defuzzification. Then, from the above interval, a suitable value can be selected as the final reasoning consequence so that some special constraints are satisfied as possibly. At last, we apply the fuzzy reasoning based on the new fuzzy rough set to the scheduling problems, and numerical computational results show that the fuzzy reasoning based on the new fuzzy rough set is more suitable for the scheduling problems compared with the fuzzy reasoning based on the CRI method and the III method.