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Extension of restricted equivalence functions and similarity measures for type-2 fuzzy sets

[PDF] Miguel_ExtensionRestricted.pdf (328.8Ko)
Identificadores
URI: https://hdl.handle.net/10481/107399
DOI: 10.1109/TFUZZ.2021.3136349
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Estadísticas
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Auteur
de Miguel, Laura; Santiago, Regivan; Wagner, Christian; Garibaldi, Jonathan M.; Takáč, Zdenko; Roldán López de Hierro, Antonio Francisco; Bustince, Humberto
Editorial
IEEE
Materia
Explainable AI
 
Information loss
 
Restricted equivalence functions
 
Similarity measures
 
Type-2 fuzzy sets
 
Uncertainty
 
Inteligencia artificial explicable
 
Pérdida de información
 
Funciones de equivalencia restringida
 
Medidas de similaridad
 
Conjunto difuso
 
Conjunto difuso de tipo 2
 
Incertidumbre
 
Date
2022-09
Referencia bibliográfica
L. de Miguel, R. Santiago, C. Wagner, J.M. Garibaldi, Z. Takáč, A.F. Roldán López de Hierro, H. Bustince (2022). Extension of restricted equivalence functions and similarity measures for type-2 fuzzy sets. IEEE Transactions on Fuzzy Systems, vol. 30, no. 9, pp. 4005-4016
Patrocinador
Research Services of Universidad Publica de Navarra under Project PID2019-108392GBI00; MCIN/AEI/10.13039/501100011033, TIN2017-89517-P; Grant VEGA 1/0267/21; U.K. EPSRC under Grant EP/P011918/1
Résumé
In this work, we generalize the notion of restricted equivalence function for type-2 fuzzy sets, leading to the notion of extended restricted equivalence functions. We also study how under suitable conditions, these new functions recover the standard axioms for restricted equivalence functions in the real setting. Extended restricted equivalence functions allow us to compare any two general type-2 fuzzy sets and to generate a similarity measure for type-2 fuzzy sets. The result of this similarity is a fuzzy set on the same referential set (i.e., domain) as the considered type-2 fuzzy set. The latter is crucial for applications such as explainable AI and decision-making, as it enables an intuitive interpretation of the similarity within the domain-specific context of the fuzzy sets. We show how this measure can be used to compare type-2 fuzzy sets with different membership functions in such a way that the uncertainty linked to type-2 fuzzy sets is not lost. This is achieved by generating a fuzzy set rather than a single numerical value. Furthermore, we also show how to obtain a numerical value for discrete referential sets.
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