Bustince Sola, Humberto

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https://academica-e.unavarra.es/handle/2454/48764

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Bustince Sola

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Humberto

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Estadística, Informática y Matemáticas

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ISC. Institute of Smart Cities

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0000-0002-1279-6195

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88384

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278

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Author: Fernández Fernández, Francisco Javier × Subject: Admissible orders ×

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Now showing 1 - 2 of 2
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    PublicationOpen Access
    On admissible orders on the set of discrete fuzzy numbers for application in decision making problems
    (MDPI, 2021) Riera, Juan Vicente; Massanet, Sebastia; Bustince Sola, Humberto; Fernández Fernández, Francisco Javier; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    The study of orders is a constantly evolving topic, not only for its interest from a theoretical point of view, but also for its possible applications. Recently, one of the hot lines of research has been the construction of admissible orders in different frameworks. Following this direction, this paper presents a new representation theorem in the field of discrete fuzzy numbers that enables the construction of two families of admissible orders in the set of discrete fuzzy numbers whose support is a closed interval of a finite chain, leading to the first admissible orders introduced in this framework.
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    PublicationOpen Access
    Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface
    (IEEE, 2021) Fumanal Idocin, Javier; Takáč, Zdenko; Fernández Fernández, Francisco Javier; Sanz Delgado, José Antonio; Goyena Baroja, Harkaitz; Lin, Chin-Teng; Wang, Yu-Kai; Bustince Sola, Humberto; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
    In this work we develop moderate deviation functions to measure similarity and dissimilarity among a set of given interval-valued data to construct interval-valued aggregation functions, and we apply these functions in two MotorImagery Brain Computer Interface (MI-BCI) systems to classify electroencephalography signals. To do so, we introduce the notion of interval-valued moderate deviation function and, in particular, we study those interval-valued moderate deviation functions which preserve the width of the input intervals. In order to apply them in a MI-BCI system, we first use fuzzy implication operators to measure the uncertainty linked to the output of each classifier in the ensemble of the system, and then we perform the decision making phase using the new interval-valued aggregation functions. We have tested the goodness of our proposal in two MI-BCI frameworks, obtaining better results than those obtained using other numerical aggregation and interval-valued OWA operators, and obtaining competitive results versus some non aggregation-based frameworks.