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    A proposal for tuning the α parameter in CαC-integrals for application in fuzzy rule-based classification systems

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    2020100426_Lucca_ProposalTuning.pdf (414.3Kb)
    Date
    2020
    Author
    Lucca, Giancarlo 
    Sanz Delgado, José Antonio Upna Orcid
    Pereira Dimuro, Graçaliz Upna Orcid
    Bedregal, Benjamin Upna Orcid
    Bustince Sola, Humberto Upna Orcid
    Version
    Acceso abierto / Sarbide irekia
    Type
    Artículo / Artikulua
    Version
    Versión aceptada / Onetsi den bertsioa
    Project Identifier
    ES/1PE/TIN2016-77356-P 
    Impact
     
     
     
    10.1007/s11047-018-9678-x
     
     
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    Abstract
    In this paper, we consider the concept of extended Choquet integral generalized by a copula, called CC-integral. In particular, we adopt a CC-integral that uses a copula defined by a parameter α, which behavior was tested in a previous work using different fixed values. In this contribution, we propose an extension of this method by learning the best value for the parameter α using a genetic algo ... [++]
    In this paper, we consider the concept of extended Choquet integral generalized by a copula, called CC-integral. In particular, we adopt a CC-integral that uses a copula defined by a parameter α, which behavior was tested in a previous work using different fixed values. In this contribution, we propose an extension of this method by learning the best value for the parameter α using a genetic algorithm. This new proposal is applied in the fuzzy reasoning method of fuzzy rule-based classification systems in such a way that, for each class, the most suitable value of the parameter α is obtained, which can lead to an improvement on the system's performance. In the experimental study, we test the performance of 4 different so called CαC-integrals, comparing the results obtained when using fixed values for the parameter α against the results provided by our new evolutionary approach. From the obtained results, it is possible to conclude that the genetic learning of the parameter α is statistically superior than the fixed one for two copulas. Moreover, in general, the accuracy achieved in test is superior than that of the fixed approach in all functions. We also compare the quality of this approach with related approaches, showing that the methodology proposed in this work provides competitive results. Therefore, we demonstrate that CαC-integrals with α learned genetically can be considered as a good alternative to be used in fuzzy rule-based classification systems. [--]
    Subject
    Aggregation functions, Choquet integral, Fuzzy rule-based classification systems, Fuzzy reasoning method, Genetic algorithms, Evolutionary fuzzy systems
     
    Publisher
    Springer
    Published in
    Natural Computing, 2020, 19(3), 533-546
    Departament
    Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas / Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute of Smart Cities - ISC / Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila
     
    Publisher version
    https://doi.org/10.1007/s11047-018-9678-x
    URI
    https://hdl.handle.net/2454/39283
    Sponsorship
    The authors would like to thank the Brazilian National Counsel of Technological and Scientific Development CNPq (Proc. 233950/2014-1, 481283/2013-7, 306970/ 2013-9, 307681/2012-2) and the Spanish Ministry of Science and Technology under project TIN2016-77356-P (AEI/FEDER, UE). G.P. Dimuro is also supported by Caixa and Fundación Caja Navarra of Spain.
    Appears in Collections
    • Artículos de revista - Aldizkari artikuluak [4926]
    • Artículos de revista ISC - ISC aldizkari artikuluak [466]
    • Artículos de revista DEIM - EIMS Aldizkari artikuluak [283]
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