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Generalized hesitant fuzzy knowledge measure with its application to multi-criteria decision-making
被引:9
作者:
Singh, Surender
[1
]
Ganie, Abdul Haseeb
[1
]
机构:
[1] Shri Mata Vaishno Devi Univ, Fac Sci, Sch Math, Katra 182320, Jammu & Kashmir, India
关键词:
Hesitant fuzzy set (HFS);
Multi-criteria decision-making;
Hesitant fuzzy entropy measure;
Hesitant fuzzy knowledge measure;
INFORMATION MEASURES;
SIMILARITY MEASURES;
ENTROPY MEASURES;
SETS;
DISTANCE;
SYSTEMS;
D O I:
10.1007/s41066-021-00263-5
中图分类号:
TP18 [人工智能理论];
学科分类号:
081104 ;
0812 ;
0835 ;
1405 ;
摘要:
Hesitant fuzzy (HF) entropy and HF-knowledge measures are two dual concepts that have similar practical applications despite different mathematical structures. In some real-life scenarios, one particular entropy/knowledge measure may not be reasonable due to some undesirable and counter-intuitive situations. In this paper, we introduce a one-parametric generalized knowledge measure in the HF-setting. Such a generalization provides a class of HF-knowledge measures and hence the flexibility in the practical problems. We show the advantages of the generalized measure over the existing HF-entropy/knowledge measures in view of weight computation in the decision-making problems and ambiguity computation of two different hesitant fuzzy elements. At last, we apply the proposed generalized knowledge measure of HFSs in multi-criteria decision-making (MCDM) using the bidirectional projection method in the hesitant fuzzy environment.
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页码:239 / 252
页数:14
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