Choquet integrals of weighted intuitionistic fuzzy information
By Zeshui Xu. Information Sciences, 2010
464 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/04e895fa2fdb8a2311b1523f800cd7a65929d45b
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In Control and Systems Engineering
In Management Science and Operations Research
In Statistics and Probability
Citations in the corpus
- Approaches to multiple attribute group decision making based on intuitionistic fuzzy power aggregation operators (2011, background): “Recently, some operators have been given to aggregate IFNs [21–23,14–16,12,13,3,27,30,20,5,17,18], such as intuitionistic fuzzy ordered weighted averaging operator, intuitionistic fuzzy ordered weighted geometric operator, and intuitionistic fuzzy hybrid aggregation operators.”
- Intuitionistic Fuzzy Information Aggregation Using Einstein Operations (2012, methodology): “Tan and Chen [53] and Xu [61] used the Choquet integral to propose some intuitionistic fuzzy aggregation operators, which not only consider the importance of the elements or their ordered positions but also can reflect the correlations among the elements or their ordered positions, and pointed out t”
- Methods for strategic decision-making problems with immediate probabilities in intuitionistic fuzzy setting (2012, methodology): “Xu [34] used the Choquet integral to propose some correlated aggregating operators for aggregating intuitionistic fuzzy values or interval-valued intuitionistic fuzzy values with correlative weights.”
- Simplified neutrosophic sets and their applications in multi-criteria group decision-making problems (2016, background): “Then two SNNs A = < T A , I A , F A > and B = < T B , I B , F B > are reduced to IFNs.”
- Fundamental Properties of Interval‐Valued Pythagorean Fuzzy Aggregation Operators (2016, unknown): “Let p̃ = ([μ−, μ+], [ν−, ν+]), p̃1 = ([μ1 , μ1 ], [ν− 1 , ν+ 1 ]), p̃2 = ([μ2 , μ + 2 ], [ν − 2 , ν + 2 ]) be three IVPFNs, and λ > 0, λ1 > 0, λ2 > 0 , then (1) p̃1 ⊕ p̃2 = p̃2 ⊕ p̃1; (2) p̃1 ⊗ p̃2 = p̃2 ⊗ p̃1; (3) λ(p̃1 ⊕ p̃2) = λp̃1 ⊕ λp̃2; (4) λ1p̃ ⊕ λ2p̃ = (λ1 + λ2)p̃; (5) (p̃1 ⊗ p̃2) = p̃ 1 ⊗ p”
- Pythagorean Fuzzy Choquet Integral Based MABAC Method for Multiple Attribute Group Decision Making (2016, methodology): “Comparing with the existing approaches, our approach has more capability than [38,39] to model the vagueness in the practical MAGDM.”
- Generation of linear orders for intervals by means of aggregation functions (2013, background): “In recent years there has been an increasing interest in the use of extensions of fuzzy sets such as interval-valued fuzzy sets [14] and Atanassov’s intuitionistic fuzzy sets [1] in many different fields such as image processing [4,7], decision making [11], classification [10] or consensus [3].”
- Some Hesitant Fuzzy Aggregation Operators with Their Application in Group Decision Making (2011, methodology): “Xu (2010); Tan and Chen (2010) applied the Choquet integral to intuitionistic fuzzy environment and proposed some intuitionistic fuzzy aggregation techniques.”
- Some issues on intuitionistic fuzzy aggregation operators based on Archimedean t-conorm and t-norm (2012, background): “Moreover, a lot of work [18,27,30, 32,33,36,39] has been done about the weight vector of the intuitionistic fuzzy aggregation operators or others [6,42].”
- Distance and similarity measures for hesitant fuzzy sets (2011, background): “Later, a number of other extensions of the above distance measures have been developed for linguistic fuzzy sets (LFSs) [38] and IFSs [10–12,15–17,24,29,30,35,39–44].”
- On distance and correlation measures of hesitant fuzzy information (2011, unknown): “Suppose that ξ : (1, 2, · · · , n) → (1, 2, · · · , n) is a permutation satisfying αξ (i) ≤ αξ (j ), βξ (i) ≤ βξ (j ), for i ≤ j, i, j = s, t ; αξ (s) ≥ αξ (t), βξ (s) ≤ βξ (t) (14)”
- Pythagorean Fuzzy Information Measures and Their Applications (2017, background): “If M, N ∈ PFSs, then the operations can be defined as follows: (1) M = {< x, νM (x), μM (x) > |x ∈ X}; (2) M ⊆ N iff ∀x ∈ X, μM (x) ≤ μN (x) and νM (x) ≥ νN (x); (3) M = N iff ∀x ∈ X, μM (x) = μN (x) and νM (x) = νN (x); (4) M = {< x, 1, 0 > |x ∈ X}; (5) ∅M = {< x, 0, 1 > |x ∈ X}; (6) M ⋂ N = {< x, ”
- Induced generalized intuitionistic fuzzy operators (2011, methodology): “(21)) becomes the interval-valued intuitionistic fuzzy Choquet ordered averaging (IVIFCOA) operator [32]: IVIFCOA hu1; ~ a1i; hu2; ~ a2i; .”