DSpace Repository :: Browsing by Author "Bustince Sola, Humberto"

Browsing by Author "Bustince Sola, Humberto"

Now showing 1 - 20 of 194

Open Access
About the intuitionistic fuzzy set generators
(Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 1997) Bustince Sola, Humberto; Mohedano Salillas, Victoria; Automática y Computación; Automatika eta Konputazioa
In this paper form the definition of intuitionistic fuzzy sets we analyze the intuitionistic fuzzy generators and the complementation in these sets. We start by defining the intuitionistic fuzzy generators in order to then study the particular cases for which this definition coincides with the fuzzy complementation. Afterwards we analyze the existence of equilibrium points, dual points and we present characterization theorems of intuitionistic fuzzy generators. Lastly, we study a manner of constructing intuitionistic fuzzy sets and analyse the structure of the complementary of intuitionistic fuzzy sets built.
Open Access
Abstract homogeneous functions and consistently influenced/disturbed multi-expert decision making
(IEEE, 2021) Santiago, Regivan; Callejas Bedregal, Benjamin; Pereira Dimuro, Graçaliz; Fernández Fernández, Francisco Javier; Bustince Sola, Humberto; Fardoun, Habib; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
In this paper we propose a new generalization for the notion of homogeneous functions. We show some properties and how it appears in some scenarios. Finally we show how this generalization can be used in order to provide a new paradigm for decision making theory called consistent influenced/disturbed decision making. In order to illustrate the applicability of this new paradigm, we provide a toy example.
Open Access
Admissible orders on fuzzy numbers
(IEEE, 2022) Zumelzu, Nicolás; Callejas Bedregal, Benjamin; Mansilla, Edmundo; Bustince Sola, Humberto; Díaz, Roberto; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
From the more than two hundred partial orders for fuzzy numbers proposed in the literature, only a few are total. In this paper, we introduce the notion of admissible order for fuzzy numbers equipped with a partial order, i.e. a total order which refines the partial order. In particular, it is given special attention to the partial order proposed by Klir and Yuan in 1995. Moreover, we propose a method to construct admissible orders on fuzzy numbers in terms of linear orders defined for intervals considering a strictly increasing upper dense sequence, proving that this order is admissible for a given partial order. Finally, we use admissible orders to ranking the path costs in fuzzy weighted graphs. IEEE
Open Access
Admissible OWA operators for fuzzy numbers
(Elsevier, 2024) García-Zamora, Diego; Cruz, Anderson; Neres, Fernando; Santiago, Regivan; Roldán López de Hierro, Antonio Francisco; Paiva, Rui; Dimuro, Graçaliz P.; Martínez López, Luis; Callejas Bedregal, Benjamin; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
Ordered Weighted Averaging (OWA) operators are some of the most widely used aggregation functions in classic literature, but their application to fuzzy numbers has been limited due to the complexity of defining a total order in fuzzy contexts. However, the recent notion of admissible order for fuzzy numbers provides an effective method to totally order them by refining a given partial order. Therefore, this paper is devoted to defining OWA operators for fuzzy numbers with respect to admissible orders and investigating their properties. Firstly, we define the OWA operators associated with such admissible orders and then we show their main properties. Afterward, an example is presented to illustrate the applicability of these AOWA operators in linguistic decision-making. In this regard, we also develop an admissible order for trapezoidal fuzzy numbers that can be efficiently applied in practice.
Open Access
Affine construction methodology of aggregation functions
(Elsevier, 2020) Roldán López de Hierro, Antonio Francisco; Roldán, Concepción; Bustince Sola, Humberto; Fernández Fernández, Francisco Javier; Rodríguez Martínez, Iosu; Fardoun, Habib; Lafuente López, Julio; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Aggregation functions have attracted much attention in recent times because of its potential use in many areas such us data fusion and decision making. In practice, most of the aggregation functions that scientists use in their studies are constructed from very simple (usually affine or polynomial) functions. However, these are distinct in nature. In this paper, we develop a systematic study of these two classes of functions from a common point of view. To do this, we introduce the class of affine aggregation functions, which cover both the aforementioned families and most of examples of aggregation functions that are used in practice, including, by its great applicability, the symmetric case. Our study allows us to characterize when a function constructed from affine or polynomial functions is, in fact, a new aggregation function. We also study when sums or products of this kind of functions are again an aggregation function.
Open Access
Aggregation and pre-aggregation functions in fuzzy rule-based classification systems
(2018) Lucca, Giancarlo; Bustince Sola, Humberto; Sanz Delgado, José Antonio; Automática y Computación; Automatika eta Konputazioa
Una manera eficiente de tratar problemas de clasificación, entre otras, es el uso de Sistemas de Clasificación Basados en Reglas Difusas (SCBRDs). Estos sistemas están compuestos por dos componentes principales, la Base de Conocimiento (BC) y el Método de Razonamiento Difuso (MRD). El MRD es el método responsable de clasificar nuevos ejemplos utilizando la información almacenada en la BC. Un punto clave del MRD es la forma en la que se agrega la información proporcionada por las reglas difusas disparadas. Precisamente, la función de agregación es lo que diferencia a los dos MRDs más utilizados de la literatura especializada. El primero, llamado de Regla Ganadora (RG), tiene un comportamiento promedio, es decir, el resultado de la agregación está en el rango delimitado por el mínimo y el máximo de los valores a agregar y utiliza la mayor relación entre el nuevo ejemplo a clasificar y las reglas. El segundo, conocido como Combinación Aditiva (CA), es ampliamente utilizado por los algoritmos difusos más precisos de la actualidad y aplica una suma normalizada para agregar toda la información relacionada con el ejemplo. Sin embargo, este método no presenta un comportamiento promedio. En este trabajo de tesis, proponemos modificar la manera en la que se agrega la información en el MRD, aplicando generalizaciones de la integral Choquet. Para ello, desarrollamos nuevos conceptos teóricos en el campo de los operadores de agregación. En concreto, definiremos generalizaciones de la Choquet integral con y sin comportamientos promedio. Utilizamos estas generalizaciones en el MRD del clasificador FARC-HD, que es un SCBRD del estado del arte. A partir de los resultados obtenidos, demostramos que el nuevo MRD puede ser utilizado, de manera eficiente, para afrontar problemas de clasificación. Además, mostramos que los resultados son estadísticamente equivalentes, o incluso superiores, a los clasificadores difusos considerados como estado del arte.
Open Access
Aggregation functions based on the Choquet integral applied to image resizing
(Atlantis Press, 2019) Bueno, Jéssica C. S.; Dias, Camila A.; Pereira Dimuro, Graçaliz; Santos, Helida; Bustince Sola, Humberto; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
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.
Open Access
Aggregation functions to combine RGB color channels in stereo matching
(Optical Society of America, 2013) Galar Idoate, Mikel; Jurío Munárriz, Aránzazu; López Molina, Carlos; Sanz Delgado, José Antonio; Paternain Dallo, Daniel; Bustince Sola, Humberto; Automática y Computación; Automatika eta Konputazioa; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
In this paper we present a comparison study between different aggregation functions for the combination of RGB color channels in stereo matching problem. We introduce color information from images to the stereo matching algorithm by aggregating the similarities of the RGB channels which are calculated independently. We compare the accuracy of different stereo matching algorithms and aggregation functions. We show experimentally that the best function depends on the stereo matching algorithm considered, but the dual of the geometric mean excels as the most robust aggregation.
Open Access
Aggregation of individual rankings through fusion functions: criticism and optimality analysis
(IEEE, 2020) Bustince Sola, Humberto; Callejas Bedregal, Benjamin; Campión Arrastia, María Jesús; Silva, Ivanoska da; Fernández Fernández, Francisco Javier; Induráin Eraso, Esteban; Raventós Pujol, Armajac; Santiago, Regivan; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
Throughout this paper, our main idea is to analyze from a theoretical and normative point of view different methods to aggregate individual rankings. To do so, first we introduce the concept of a general mean on an abstract set. This new concept conciliates the social choice where well-known impossibility results as the Arrovian ones are encountered and the decision-making approaches where the necessity of fusing rankings is unavoidable. Moreover it gives rise to a reasonable definition of the concept of a ranking fusion function that does indeed satisfy the axioms of a general mean. Then we will introduce some methods to build ranking fusion functions, paying a special attention to the use of score functions, and pointing out the equivalence between ranking and scoring. To conclude, we prove that any ranking fusion function introduces a partial order on rankings implemented on a finite set of alternatives. Therefore, this allows us to compare rankings and different methods of aggregation, so that in practice one should look for the maximal elements with respect to such orders defined on rankings IEEE.
Open Access
An algorithm for group decision making using n -dimensional fuzzy sets, admissible orders and OWA operators
(Elsevier, 2017) Miguel Turullols, Laura de; Sesma Sara, Mikel; Elkano Ilintxeta, Mikel; Asiain Ollo, María José; Bustince Sola, Humberto; Automatika eta Konputazioa; Matematika; Institute of Smart Cities - ISC; Automática y Computación; Matemáticas; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
In this paper we propose an algorithm to solve group decision making problems using n-dimensional fuzzy sets, namely, sets in which the membership degree of each element to the set is given by an in- creasing tuple of n elements. The use of these sets has naturally led us to define admissible orders for n-dimensional fuzzy sets, to present a construction method for those orders and to study OWA operators for aggregating the tuples used to represent the membership degrees of the elements. In these condi- tions, we present an algorithm and apply it to a case study, in which we show that the exploitation phase which appears in many decision making methods can be omitted by just considering linear orders between tuples.
Open Access
Análisis de redes sociales basado en las conquistas de César Borgia
(Universidad de Málaga, 2021) Fumanal Idocin, Javier; Cordón, Óscar; Alonso Betanzos, Amparo; Bustince Sola, Humberto; Fernández Fernández, Francisco Javier; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
En este trabajo presentamos el modelado de redes sociales y detección de comunidades utilizando como base un evento histórico real, las conquistas de César Borgia en el siglo XV. Para ello, proponemos un nuevo conjunto de funciones, llamadas funciones de afinidad, disenadas para capturar la 'naturaleza de las interacciones locales entre cada par de actores en una red. Utilizando estas funciones, desarrollamos un nuevo algoritmo de detección de comunidades, el Borgia Clustering, donde las comunidades surgen naturalmente de un proceso de simulación de interacción de múltiples agentes en la red. También discutimos los efectos del tamaño y la escala de cada comunidad, y como pueden ser tomadas en cuenta en el proceso de simulación. Finalmente, comparamos nuestra detección de comunidades con otros algoritmos representativos, encontrando resultados favorables a nuestra propuesta.
Open Access
Aplicación del aprendizaje profundo para la predicción de caudal frente a escenarios de inundación del río Arga
(2023) Moreno Lasa, Ismael; Bustince Sola, Humberto; Escuela Técnica Superior de Ingeniería Agronómica y Biociencias; Nekazaritzako Ingeniaritzako eta Biozientzietako Goi Mailako Eskola Teknikoa
La modelización del caudal en cuencas hidrográficas de rápido flujo es un problema altamente complejo en el que los modelos hidrológicos comúnmente utilizados a menudo tienen limitaciones. La existencia de una predicción que permita una alerta temprana de inundaciones es vital para minimizar los daños a la propiedad y la infraestructura, y reducir los riesgos potenciales para las personas. Las técnicas de aprendizaje automático tienen el potencial de superar algunas de las limitaciones de los modelos hidrológicos tradicionales al utilizar conjuntos de datos grandes para aprender las relaciones entre diferentes variables hidrológicas, lo que permite realizar predicciones más precisas del caudal en cuencas de flujo rápido. El objetivo de este trabajo ha sido aplicar redes neuronales de Memoria a Corto y Largo Plazo (LSTM, por sus siglas en inglés) para la predicción de caudal en la cuenca del río Arga. Las redes LSTM son un tipo de Redes Neuronales Recurrentes (RNN) que son especialmente adecuadas para tareas de predicción en series temporales. Estas redes tienen células de memoria que les permiten recordar patrones en los datos a lo largo de un período de tiempo más largo, lo que las hace efectivas para capturar las dependencias temporales presentes en los datos de caudal. El uso de este tipo de redes permite superar algunas de las limitaciones típicas de los modelos hidrológicos tradicionales. Al utilizar redes LSTM, mostramos que el modelo es capaz de capturar la compleja dinámica temporal de los datos de caudal y realizar predicciones precisas a corto plazo, incluso para escenarios de alto flujo, con varias horas de anticipación. Los resultados demuestran que el uso de estas redes para la predicción del caudal en la cuenca del río Arga es un enfoque prometedor, especialmente para predicciones a corto plazo, con anticipación de horas. El uso de enfoques de aprendizaje automático puede desbloquear un nuevo potencial en la predicción y gestión de los recursos hídricos en el área, así como en la evaluación de riesgos y sistemas de alerta temprana para inundaciones.
Open Access
Aplicación web para la recogida y consulta de información de lesiones colorrectales de pacientes
(2021) Guerra Trapiella, Juana María; Bustince Sola, Humberto; Rodríguez Martínez, Iosu; Escuela Técnica Superior de Ingeniería Industrial, Informática y de Telecomunicación; Industria, Informatika eta Telekomunikazio Ingeniaritzako Goi Mailako Eskola Teknikoa
Este proyecto surgió tras una propuesta realizada por Navarrabiomed, en concreto por la unidad de investigación en endoscopia digestiva. Se buscaba obtener una herramienta de almacenamiento de información clínica, morfológica y patológica de sus pacientes en diferentes momentos: evaluación de lesiones, evaluación de cicatrices, evaluación de cicatrices si se presenta alguna complicación, seguimiento pasados 3-6 meses y seguimiento pasados 15-18 meses. Además de que posibilitase adjuntar imágenes endoscópicas asociadas a cada momento. Este proyecto se centra en la creación de dicha aplicación web, así como en la creación de la base de datos MySQL. En esta se almacenará la información clínica clave de los pacientes en sus diferentes momentos junto a sus imágenes endoscópicas. El objetivo es que esta aplicación sea el primer paso dentro de un proyecto a mayor escala que explotará la información recolectada tras su uso. En el proyecto a mayor escala se aplicarán técnicas de IA para mejorar la precisión de los diagnósticos y la eficacia y seguridad de los tratamientos endoscópicos que se aplican. Toda esta idea conjunta ha sido premiada con el primer premio del “Concurso Medtech Navarra” [1]. Se ha aplicado un desarrollo iterativo e incremental en el que han tenido un papel fundamental las entrevistas realizadas con los médicos del Equipo de Navarrabiomed al frente del proyecto. Además, se ha elegido su desarrollo en el Framework PHP Laravel ya que posee numerosas posibilidades en su uso, las cuales han sido empleadas siendo muy útiles.
Open Access
Application and comparison of CC-integrals in business group decision making
(Springer, 2022) Wieczynski, Jonata; Lucca, Giancarlo; Borges, Eduardo N.; Pereira Dimuro, Graçaliz; Lourenzutti, Rodolfo; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Optimized decisions is required by businesses (analysts) if they want to stay open. Even thought some of these are from the knowhow of the managers/executives, most of them can be described mathematically and solved (semi)-optimally by computers. The Group Modular Choquet Random Technique for Order of Preference by Similarity to Ideal Solution (GMC-RTOPSIS) is a Multi-Criteria Decision Making (MCDM) that was developed as a method to optimize the later types of problems, by being able to work with multiple heterogeneous data types and interaction among different criteria. On the other hand the Choquet integral is widely used in various fields, such as brain-computer interfaces and classification problems. With the introduction of the CC-integrals, this study presents the GMC-RTOPSIS method with CC-integrals. We applied 30 different CC-integrals in the method and analyzed its results using 3 different methods. We found that by modifying the decisionmaking method we allow for more flexibility and certainty in the choosing process.
Open Access
Application of two different methods for extending lattice-valued restricted equivalence functions used for constructing similarity measures on L-fuzzy sets
(Elsevier, 2018) Palmeira, Eduardo S.; Callejas Bedregal, Benjamin; Bustince Sola, Humberto; Paternain Dallo, Daniel; Miguel Turullols, Laura de; Automatika eta Konputazioa; Institute of Smart Cities - ISC; Automática y Computación; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Based on previous investigations, we have proposed two different methods to extend lattice-valued fuzzy connectives (t-norms, t-conorms, negations and implications) and other related operators, considering a generalized notion of sublattices. Taking into account the results obtained and seeking to analyze the behavior of both extension methods in face of fuzzy operators related to image processing, we have applied these methods so as to extend restricted equivalence functions, restricted dissimilarity functions and Ee,N-normal functions. We also generalize the concepts of similarity measure, distance measure and entropy measure for L-fuzzy sets constructing them via restricted equivalence functions, restricted dissimilarity functions and Ee,N-normal functions
Open Access
Applications of finite interval-valued hesitant fuzzy preference relations in group decision making
(Elsevier, 2016) Pérez Fernández, Raúl; Alonso, Pedro; Bustince Sola, Humberto; Díaz, Irene; Montes Rodríguez, Susana; Automática y Computación; Automatika eta Konputazioa
The main purpose of this paper is to present the twofold group decision making problem, which is a new point of view of the group decision making problem where several experts and criteria can be considered at the same time. This problem is based on the study of finitely generated sets and finite interval-valued hesitant fuzzy preference relations. Furthermore, the Extended Weighted Voting Method, which is used in the exploitation phase of a classical group decision making problem, is generalized to the twofold case.
Open Access
Applying d-XChoquet integrals in classification problems
(IEEE, 2022) Wieczynski, Jonata; Lucca, Giancarlo; Borges, Eduardo N.; Emmendorfer, Leonardo R.; Ferrero Jaurrieta, Mikel; Pereira Dimuro, Graçaliz; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Several generalizations of the Choquet integral have been applied in the Fuzzy Reasoning Method (FRM) of Fuzzy Rule-Based Classification Systems (FRBCS's) to improve its performance. Additionally, to achieve that goal, researchers have searched for new ways to provide more flexibility to those generalizations, by restricting the requirements of the functions being used in their constructions and relaxing the monotonicity of the integral. This is the case of CT-integrals, CC-integrals, CF-integrals, CF1F2-integrals and dCF-integrals, which obtained good performance in classification algorithms, more specifically, in the fuzzy association rule-based classification method for high-dimensional problems (FARC-HD). Thereafter, with the introduction of Choquet integrals based on restricted dissimilarity functions (RDFs) in place of the standard difference, a new generalization was made possible: the d-XChoquet (d-XC) integrals, which are ordered directional increasing functions and, depending on the adopted RDF, may also be a pre-aggregation function. Those integrals were applied in multi-criteria decision making problems and also in a motor-imagery brain computer interface framework. In the present paper, we introduce a new FRM based on the d-XC integral family, analyzing its performance by applying it to 33 different datasets from the literature.
Open Access
ARTxAI: explainable artificial intelligence curates deep representation learning for artistic images using fuzzy techniques
(IEEE, 2024) Fumanal Idocin, Javier; Andreu-Perez, Javier; Cordón, Óscar; Hagras, Hani; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Automatic art analysis employs different image processing techniques to classify and categorize works of art. When working with artistic images, we need to take into account further considerations compared to classical image processing. This is because artistic paintings change drastically depending on the author, the scene depicted, and their artistic style. This can result in features that perform very well in a given task but do not grasp the whole of the visual and symbolic information contained in a painting. In this article, we show how the features obtained from different tasks in artistic image classification are suitable to solve other ones of similar nature. We present different methods to improve the generalization capabilities and performance of artistic classification systems. Furthermore, we propose an explainable artificial intelligence method to map known visual traits of an image with the features used by the deep learning model considering fuzzy rules. These rules show the patterns and variables that are relevant to solve each task and how effective is each of the patterns found. Our results show that compared to multitask learning, our proposed context-aware features can achieve up to 19% more accurate results when using the residual network architecture and 3% when using ConvNeXt. We also show that some of the features used by these models can be more clearly correlated to visual traits in the original image than other kinds of features.
Open Access
CC-separation measure applied in business group decision making
(SciTePress, 2021) Wieczynski, Jonata; Lucca, Giancarlo; Borges, Eduardo N.; Pereira Dimuro, Graçaliz; Lourenzutti, Rodolfo; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
In business, one of the most important management functions is decision making The Group Modular Choquet Random TOPSIS (GMC-RTOPSIS) is a Multi-Criteria Decision Making (MCDM) method that can work with multiple heterogeneous data types. This method uses the Choquet integral to deal with the interaction between different criteria. The Choquet integral has been generalized and applied in various fields of study, such as imaging processing, brain-computer interface, and classification problems. By generalizing the so-called extended Choquet integral by copulas, the concept of CC-integrals has been introduced, presenting satisfactory results when used to aggregate the information in Fuzzy Rule-Based Classification Systems. Taking this into consideration, in this paper. we applied 11 different CC-integrals in the GMC-RTOPSIS. The results demonstrated that this approach has the advantage of allowing more flexibility and certainty in the choosing process by giving a higher separation between the first and second-ranked alternatives.
Open Access
CFM-BD: a distributed rule induction algorithm for building compact fuzzy models in Big Data classification problems
(IEEE, 2020) Elkano Ilintxeta, Mikel; Sanz Delgado, José Antonio; Barrenechea Tartas, Edurne; Bustince Sola, Humberto; Galar Idoate, Mikel; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
Interpretability has always been a major concern for fuzzy rule-based classifiers. The usage of human-readable models allows them to explain the reasoning behind their predictions and decisions. However, when it comes to Big Data classification problems, fuzzy rule based classifiers have not been able to maintain the good tradeoff between accuracy and interpretability that has characterized these techniques in non-Big-Data environments. The most accurate methods build models composed of a large number of rules and fuzzy sets that are too complex, while those approaches focusing on interpretability do not provide state-of-the-art discrimination capabilities. In this paper, we propose a new distributed learning algorithm named CFM-BD to construct accurate and compact fuzzy rule-based classification systems for Big Data. This method has been specifically designed from scratch for Big Data problems and does not adapt or extend any existing algorithm. The proposed learning process consists of three stages: Preprocessing based on the probability integral transform theorem; rule induction inspired by CHI-BD and Apriori algorithms; and rule selection by means of a global evolutionary optimization. We conducted a complete empirical study to test the performance of our approach in terms of accuracy, complexity, and runtime. The results obtained were compared and contrasted with four state-of-the-art fuzzy classifiers for Big Data (FBDT, FMDT, Chi-Spark-RS, and CHI-BD). According to this study, CFM-BD is able to provide competitive discrimination capabilities using significantly simpler models composed of a few rules of less than three antecedents, employing five linguistic labels for all variables.