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    Enhancing the efficiency of the interval-valued fuzzy rule-based classifier with tuning and rule selection

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    2020100263_Sanz_EnhancingEfficiency.pdf (300.9Kb)
    Date
    2020
    Author
    Sanz Delgado, José Antonio Upna Orcid
    Asmus, Tiago Upna Orcid
    Osa Hernández, Borja de la Upna
    Bustince Sola, Humberto Upna Orcid
    Version
    Acceso abierto / Sarbide irekia
    Type
    Contribución a congreso / Biltzarrerako ekarpena
    Version
    Versión aceptada / Onetsi den bertsioa
    Project Identifier
    ES/1PE/TIN2016-77356-P 
    Impact
     
     
     
    10.1007/978-3-030-50153-2_35
     
     
     
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    Abstract
    Interval-Valued fuzzy rule-based classifier with TUning and Rule Selection, IVTURS, is a state-of-the-art fuzzy classifier. One of the key point of this method is the usage of interval-valued restricted equivalence functions because their parametrization allows one to tune them to each problem, which leads to obtaining accurate results. However, they require the application of the exponentiation ... [++]
    Interval-Valued fuzzy rule-based classifier with TUning and Rule Selection, IVTURS, is a state-of-the-art fuzzy classifier. One of the key point of this method is the usage of interval-valued restricted equivalence functions because their parametrization allows one to tune them to each problem, which leads to obtaining accurate results. However, they require the application of the exponentiation several times to obtain a result, which is a time demanding operation implying an extra charge to the computational burden of the method. In this contribution, we propose to reduce the number of exponentiation operations executed by the system, so that the efficiency of the method is enhanced with no alteration of the obtained results. Moreover, the new approach also allows for a reduction on the search space of the evolutionary method carried out in IVTURS. Consequently, we also propose four different approaches to take advantage of this reduction on the search space to study if it can imply an enhancement of the accuracy of the classifier. The experimental results prove: 1) the enhancement of the efficiency of IVTURS and 2) the accuracy of IVTURS is competitive versus that of the approaches using the reduced search space. [--]
    Subject
    Interval-valued fuzzy rule-based classification systems, Interval-valued fuzzy sets, Interval type-2 fuzzy sets, Evolutionary fuzzy systems
     
    Publisher
    Springer
    Published in
    Lesot MJ. et al. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2020. Communications in Computer and Information Science, vol 1239. Springer, Cham, p. 463-478. ISBN: 978-3-030-50153-2
    Description
    Trabajo presentado a la 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020 (15-19 de junio de 2020)
    Departament
    Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. ISC - Institute of Smart Cities
    Publisher version
    https://doi.org/10.1007/978-3-030-50153-2_35
    URI
    https://hdl.handle.net/2454/39550
    Sponsorship
    This work was supported in part by the Spanish Ministry of Science and Technology (project TIN2016-77356-P (AEI/FEDER, UE)) and the Public University of Navarre under the project PJUPNA1926.
    Appears in Collections
    • Comunicaciones y ponencias de congresos - Biltzarrak eta Argitalpenak [571]
    • Comunicaciones y ponencias de congresos ISC - ISC biltzarretako komunikazioak eta txostenak [117]
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