Hesitant Fuzzy Sets: State of the Art and Future Directions

By Rosa M. Rodríguez, Luis Martínez-López, Vicenç Torra, Zeshui Xu, Francisco Herrera. International Journal of Intelligent Systems, 2014

455 citations recorded by the source corpus.

The necessity of dealing with uncertainty in real world problems has been a long‐term research challenge that has originated different methodologies and theories. Fuzzy sets along with their extensions, such as type‐2 fuzzy sets, interval‐valued fuzzy sets, and Atanassov's intuitionistic fuzzy sets, have provided a wide range of tools that are able to deal with uncertainty in different types of problems. Recently, a new extension of fuzzy sets so‐called hesitant fuzzy sets has been introduced to deal with hesitant situations, which were not well managed by the previous tools. Hesitant fuzzy sets have attracted very quickly the attention of many researchers that have proposed diverse extensions, several types of operators to compute with such types of information, and eventually some applications have been developed. Because of such a growth, this paper presents an overview on hesitant fuzzy sets with the aim of providing a clear perspective on the different concepts, tools and trends related to this extension of fuzzy sets.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4ebe3a0ab337ec0b7f06a783e6798a4c56f10503

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Citations in the corpus

A Historical Account of Types of Fuzzy Sets and Their Relationships (2016, methodology): “- Recently, HFSs have been used in group decision making problems [123], and extended to Computing with Words [121], [122].”
Interval-valued hesitant fuzzy linguistic sets and their applications in multi-criteria decision-making problems (2014, background): “[43] presented an overview and discussed future trends for HFSs.”
Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets (2015, background): “In quantitative settings, when an expert considers several values to determine the membership degree of an element to a set, the concept of hesitant fuzzy set (HFS) was introduced [33,34].”