A fusion approach for managing multi-granularity linguistic term sets in decision making

By Francisco Herrera, Enrique Herrera-Viedma, Luis Martínez-López. 2000

844 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research

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Citations in the corpus

A survey of data fusion in smart city applications (2019, methodology): “General techniques include Bayesian inference [66], Dempster-Shafer Inference [67], and semantic approaches [68]. anomaly detection [73] and others [38].”
Encoding Words Into Interval Type-2 Fuzzy Sets Using an Interval Approach (2008, background): “, [1], [4], [9], [12], [26], [27], [29], [30], and [34]–[38]; however, as claimed in [12]–[15] and [17], “Words mean different things to different people, and so are uncertain.”
Hesitant fuzzy power aggregation operators and their application to multiple attribute group decision making (2013, background): “In practice, we often encounter situations in which the input arguments cannot be expressed as exact numerical values; instead, they might take the form of interval numbers [60], intuitionistic fuzzy numbers [6,7,15,42,58], interval-valued intuitionistic fuzzy numbers [51], linguistic variables [1,8”
A Consensus Support System Model for Group Decision-Making Problems With Multigranular Linguistic Preference Relations (2005, methodology): “For example, we can use a BLTS with 15 terms symmetrically distributed [ 10 ].”
The role of fuzzy sets in decision sciences: Old techniques and new directions (2011, background): “Linguistic variables [12] are supposed to model human originated information , so as to get decision methods closer to the user cognition [13].”
Deviation measures of linguistic preference relations in group decision making (2005, methodology): “Let us suppose an investment company, which wants to invest a sum of money in the best option (adapted from [25]).”
Multiple criteria decision making (MCDM) methods in economics: an overview (2011, methodology): “In the area of fuzzy reasoning, the two–tuple linguistic representation method (Herrera et al. 2000; Liu and Zhang 2011) is widely applied for computing with words.”
A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making (2001, methodology): “Depending on the uncertainty degree held by a source of information qualifying a phenomenon, the linguistic term set will have more or less terms [6], [1].”
Consensus reaching in social network group decision making: Research paradigms and challenges (2018, background): “Decision makers' preferences on a set of alternatives X can be represented in different ways using distinct preference representation structures, such as: utility values [22]; preference orderings [23,24]; additive preference relations [25-27]; linguistic preferences [28-32]; multiple attribute pref”
Computing with words in decision making: foundations, trends and prospects (2009, background): “In [41] an approach to fusion multi-granular information in decision making was presented.”
An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges (2012, background): “The aim of this section is to review the foundations of the 2-tuple linguistic representation model both representation and computing models and afterwards to show the use of this model on the main research branches in which it has been applied.”
Interval-valued hesitant fuzzy linguistic sets and their applications in multi-criteria decision-making problems (2014, methodology): “The linguistic term sh(a) can be transformed into a trapezoidal fuzzy number [a, b, c, d] by the method used in [13].”