Representation, optimization and generation of fuzzy measures

We review recent literature on three aspects of fuzzy measures: their representations, learning optimal fuzzy measures and random generation of various types of fuzzy measures. These three aspects are interdependent: methods of learning fuzzy measures depend on their representation, and may also inc...

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Veröffentlicht in:Information fusion Jg. 106; S. 102295
Hauptverfasser: Beliakov, Gleb, Wu, Jian-Zhang, Ding, Weiping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.06.2024
Schlagworte:
Equivalent representations
Fuzzy measure
Fuzzy measure identification
Optimization
Random generation
Equivalent representations
Fuzzy measure
Fuzzy measure identification
Optimization
Random generation
ISSN:1566-2535, 1872-6305
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Zusammenfassung:We review recent literature on three aspects of fuzzy measures: their representations, learning optimal fuzzy measures and random generation of various types of fuzzy measures. These three aspects are interdependent: methods of learning fuzzy measures depend on their representation, and may also include random generation as one of the steps, on the other hand different representations also affect generation methods, while random generation plays an important role in simulation studies for post-hoc analysis of sets of measures learned from data and problem-specific constraints. Explicit modelling of interactions between the decision variables is a distinctive feature of integrals based on fuzzy measures, but its price is high computational complexity. To extend their range of applicability efficient representations and computational techniques are required. All three mentioned aspects provide mathematical and computational tools for novel applications of fuzzy measures and integrals in decision making and information fusion, allow scaling up significantly the domain of applicability and reduce their complexity. •Recent developments on three aspects of fuzzy measures surveyed.•New efficient methods of learning fuzzy measures from data reviewed.•New methods of random generation fuzzy measures compared.
ISSN:1566-2535
1872-6305
DOI:10.1016/j.inffus.2024.102295