Aggregation Functions: A Guide for Practitioners

By Gleb Beliakov, Ana Pradera, Tomasa Calvo. Studies in Fuzziness and Soft Computing, 2007

1,520 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/c358812e61d158172bc640763469497fc3a20d81

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Citations in the corpus

New decision-making techniques and their application in the selection of financial products (2010, methodology): “The formulation of these families is straightforward, as indicated in Remarks 2–7 and in [1–4,22–24,27–30,34,35,37,40,48–51,54,55].”
Some issues on intuitionistic fuzzy aggregation operators based on Archimedean t-conorm and t-norm (2012, methodology): “…fuzzy sets, study their properties and relationships, and based on which, we study the properties of the aggregation principle proposed by Beliakov et al. [3], and give some specific intuitionistic fuzzy aggregation operators, which can be considered as the extensions of the known ones.”
The Induced Generalized OWA Operator (2009, background): “Further studies on these generalizations are found in [4-5].”
Generalized Orthopair Fuzzy Sets (2017, background): “We recall that Sugμ(F) ∈ [0, 1] and that Sug(F) is an aggregation operator [25], [34].”
Pythagorean fuzzy subsets (2013, background): “In [9] authors consider a number of important classes aggregation operators.”
Intuitionistic Fuzzy Information Aggregation Using Einstein Operations (2012, background): “4 [12], [31]: An aggregation function f : [0, 1] → [0, 1] is a function nondecreasing in each argument and satisfying f(0, 0, .”
”Aggregation Functions”, Cambridge University Press (2008, background): “…as characterizations of various families of aggregation functions), and many connections have been done with either related fields or former works (such as triangular norms in probabilistic metric spaces, theory of means and averages, etc.), see [1], [2], [3], [4], [5], [6], [7], [8], [9], [10].”
Intuitionistic Fuzzy Bonferroni Means (2011, neutral): “In [1], Bonferroni originally introduced a mean-type aggregation operator, called the Bonferroni mean (BM) [7], [8], which can provide for the aggregation lying between the max and min operators and the logical “or” and “and” operators.”
Some Hamacher Aggregation Operators Based on the Interval-Valued Intuitionistic Fuzzy Numbers and Their Application to Group Decision Making (2014, background): “Hamacher t-conorm and t-norm, which are the generalization of algebraic and Einstein t-conorm and t-norm [6], are more general and more flexible.”
The role of fuzzy sets in decision sciences: Old techniques and new directions (2011, background): “Fuzzy sets connectives have triggered a considerable development of aggregation operators for decision evaluation [7, 9, 8].”
When to use what: Methods for weighting and aggregating sustainability indicators (2017, positive): “In general, classification schemes include those based on the semantics of aggregation (Beliakov et al., 2007; Grabisch, 2009) and those based on the degree of permission of compensation (OECD, 2008).”
Pythagorean Membership Grades in Multicriteria Decision Making (2014, background): “In [3], the authors consider a number of important classes of aggregation operators.”