Bustince Sola, Humberto

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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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Now showing 1 - 2 of 2
  • PublicationOpen Access
    A fusion method for multi-valued data
    (Elsevier, 2021) Papčo, Martin; Rodríguez Martínez, Iosu; Fumanal Idocin, Javier; Altalhi, A. H.; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    In this paper we propose an extension of the notion of deviation-based aggregation function tailored to aggregate multidimensional data. Our objective is both to improve the results obtained by other methods that try to select the best aggregation function for a particular set of data, such as penalty functions, and to reduce the temporal complexity required by such approaches. We discuss how this notion can be defined and present three illustrative examples of the applicability of our new proposal in areas where temporal constraints can be strict, such as image processing, deep learning and decision making, obtaining favourable results in the process.
  • PublicationOpen Access
    Moderate deviation and restricted equivalence functions for measuring similarity between data
    (Elsevier, 2019) Altalhi, A. H.; Forcén Carvalho, Juan Ignacio; Pagola Barrio, Miguel; Barrenechea Tartas, Edurne; Bustince Sola, Humberto; Takáč, Zdenko; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
    In this work we study the relation between moderate deviation functions, restricted dissimilarity functions and restricted equivalence functions. We use moderate deviation functions in order to measure the similarity or dissimilarity between a given set of data. We show an application of moderate deviate functions used in the same way as penalty functions to make a final decision from a score matrix in a classification problem.