Lafuente López, Julio

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https://academica-e.unavarra.es/handle/2454/49347

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Lafuente López

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Julio

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Estadística, Informática y Matemáticas

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0000-0001-5997-975X

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88321

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1

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2019 - 201912020 - 20241
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Author: Mesiar, Radko × Subject: Aggregation function ×

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Now showing 1 - 2 of 2
  • Thumbnail Image
    PublicationOpen Access
    Directions of directional, ordered directional and strengthened ordered directional increasingness of linear and ordered linear fusion operators
    (IEEE, 2019) Sesma Sara, Mikel; Marco Detchart, Cedric; Lafuente López, Julio; Roldán López de Hierro, Antonio Francisco; Mesiar, Radko; Bustince Sola, Humberto; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
    In this work we discuss the forms of monotonicity that have been recently introduced to relax the monotonicity condition in the definition of aggregation functions. We focus on directional, ordered directional and strengthened ordered directional monotonicity, study their main properties and provide some results about their links and relations among them. We also present two families of functions, the so-called linear fusion functions and ordered linear fusion functions and we study the set of directions for which these types of functions are directionally, ordered directionally and strengthened ordered directionally increasing. In particular, OWA operators are an example of ordered linear fusion functions.
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    PublicationOpen Access
    A new family of aggregation functions for intervals
    (Springer, 2024) Díaz-Vázquez, Susana; Torres-Manzanera, Emilio; Rico, Noelia; Mesiar, Radko; Rodríguez Martínez, Iosu; Lafuente López, Julio; Díaz, Irene; Montes Rodríguez, Susana; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    Aggregation operators are unvaluable tools when different pieces of information have to be taken into account with respect to the same object. They allow to obtain a unique outcome when different evaluations are available for the same element/object. In this contribution we assume that the opinions are not given in form of isolated values, but intervals. We depart from two “classical” aggregation functions and define a new operator for aggregating intervals based on the two original operators. We study under what circumstances this new function is well defined and we provide a general characterization for monotonicity. We also study the behaviour of this operator when the departing functions are the most common aggregation operators. We also provide an illustrative example demonstrating the practical application of the theoretical contribution to ensemble deep learning models.