Search

Grouping functions and their dual counterpart, overlap functions, have drawn the attention of many authors, mainly because they constitute a richer class of operators compared to other types of aggregation functions. Grouping functions are a useful theoretical tool to be applied in various problems, like decision making based on fuzzy preference relations. In pairwise comparisons, for instance, those functions allow one to convey the measure of the amount of evidence in favor of either of two given alternatives. Recently, some generalizations of grouping functions were proposed, such as (i) the n-dimensional grouping functions and the more flexible general grouping functions, which allowed their application in n-dimensional problems, and (ii) n-dimensional and general interval-valued grouping functions, in order to handle uncertainty on the definition of the membership functions in real-life problems. Taking into account the importance of interval-valued fuzzy implication functions in several application problems under uncertainty, such as fuzzy inference mechanisms, this paper aims at introducing a new class of interval-valued fuzzy material implication functions. We study their properties, characterizations, construction methods and provide examples. upported by CNPq (301618/2019-4, 311429/2020-3), FAPERGS (19/2551-0001660-3), UFERSA, the Spanish Ministry of Science and Technology (TIN2016-77356-P, PID2019-108392GB I00 (MCIN/AEI/10.13039/501100011033)) and Navarra de Servicios y Tecnologías, S.A. (NASERTIC).

En este trabajo presentamos una definición de la integral de Choquet discreta n-dimensional, para fusionar datos vectoriales. Como aplicación, utilizamos estas nuevas integrales de Choquet discretas multidimensionales en la fusión de información secuencial en las redes neuronales recurrentes, mejorando los resultados obtenidos mediante el método de agregación tradicional. Este trabajo ha sido financiado por la Agencia Estatal de Investigación (España) bajo el proyecto PID2019-108392GBI00 (AEI/10.13039/ 501100011033).

Human sciences have traditionally relied on human reasoning and intelligence to infer knowledge from a wide range of sources, such as oral and written narrations, reports, and traditions. Here we develop an extension of classical social network analysis approaches to incorporate the concept of meaning in each actor, as a mean to quantify and infer further knowledge from the original source of the network. This extension is based on a new affinity function, the semantic affinity, that establishes fuzzy-like relationships between the different actors in the network, using combinations of affinity functions. We also propose a new heuristic algorithm based on the shortest capacity problem to compute this affinity function. We use these concept of meaning and semantic affinity to analyze and compare the gods and heroes from three different classical mythologies: Greek, Celtic and Nordic. We study the relationships of each individual mythology and those of common structure that is formed when we fuse the three of them. We show a strong connection between the Celtic and Nordic gods and that Greeks put more emphasis on heroic characters rather than deities. Our approach provides a technique to highlight and quantify important relationships in the original domain of the network not deducible from its structural properties.

Las interfaces cerebro-ordenador (BCI) basadas en el análisis de Electroencefalografía (EEG) están compuestas por varios elementos para procesar y clasificar las señales de entrada del cerebro. Una fase relevante de estos sistemas es el módulo de toma de decisiones, en el que la salida de diferentes clasificadores se fusiona en uno solo. En este trabajo proponemos el uso de funciones basadas en desviaciones moderadas con ponderaciones para la fase de toma de decisiones del sistema de BCI de fusión multimodal mejorado (EMF). Las funciones de agregación basadas en desviación moderada (MD) nos permiten elegir el mejor valor para agregar un vector de puntos utilizando una función de desviación moderada. Usando una MD ponderada, también podemos tener en cuenta la importancia relativa de cada dimensión en los datos multidimensionales que estamos agregando. Utilizando estas funciones en el EMF, podemos ponderar cada una de las diferentes señales cerebrales según su importancia, y utilizando la diferenciación automática, también podemos optimizarlas para el problema concreto a solucionar. La investigación de Javier Fumanal Idocin, A.Urio y Humberto Bustince ha sido apoyada por el proyecto PID2019-108392GB I00 (AEI / 10.13039 / 501100011033). La investigación de Carmen Vidaurre ha sido financiada por el proyecto RyC-2014-15671.

The rising volume of data and its high complexity has brought the need of developing increasingly efficient knowledge extraction techniques, which demands efficiency both in computational cost and in accuracy. Most of problems that are handled by these techniques has complex information to be identified. So, machine learning methods are frequently used, where a variety of functions can be applied in the different steps that are employed in their architecture. One of them is the use of aggregation functions aiming at resizing images. In this context, we introduce a study of aggregation functions based on the Choquet integral, whose main characteristic in comparison with other aggregation functions is that it considers, through fuzzy measure, the interaction between the elements to be aggregated. Thus, our main goal is to present an evaluation study of the performance of the standard Choquet integral the and copula-based generalization of the Choquet integral in relation to the maximum and mean functions, looking for results that may be better than the aggregation functions commonly applied. The results of such comparisons are promising, when evaluated through image quality metrics. The authors Camila Dias and Jessica Bueno thank CAPES for the financial support received. Gracaliz P. Dimuro has funding from CNPq/Brazil (process number 305882/20163). Eduardo N. Borges has funding from FAPERGS (TO 17/2551-0000872-3). Humberto Bustince is supported by the Spanish Ministry of Science and Technology (under project TIN2016-77356-P (AEI/FEDER, UE)).