Open Access
T-overlap t-migrative functions: a generalization of migrativity in t-overlap functions
(Universidad Distrital Francisco José de Caldas (Colombia), 2020) Zapata, Hugo; Bustince Sola, Humberto; Pereira Dimuro, Graçaliz; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Este artículo introduce una generalización de funciones migrativas por extensión de la condición de la operación producto aplicada en las variables. Más específicamente, en lugar de exigir multiplicar la variable x por un número real alfa; en este trabajo se trabaja este número alfa con las variables de acuerdo a una t-norma. Se denomina a esta generalización función t-migrativa con respecto a tal tnorma. Luego se analizan las propiedades principales de funciones t-migrativas en funciones t-overlap y se introducen algunos métodos de construcción de este tipo de funciones.
Open Access
On admissible orders over closed subintervals of [0,1]
(Elsevier, 2020) Santana, Fagner; Bedregal, Benjamin; Viana, Petrucio; Bustince Sola, Humberto; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
In this paper, we make some considerations about admissible orders on the set of closed subintervals of the unit interval I[0,1], i.e. linear orders that refine the product order on intervals. We propose a new way to generate admissible orders on I[0,1] which is more general than those we find in the current literature. Also, we deal with the possibility of an admissible order on I[0,1] to be isomorphic to the usual order on [0,1]. We prove that some orders constructed by our method are not isomorphic to the usual one and we make some considerations about the following question: is there some admissible order on I[0,1] isomorphic to the usual order on [0,1]?
Open Access
Extensions of fuzzy sets in image processing: an overview
(EUSFLAT, 2011) Pagola Barrio, Miguel; Barrenechea Tartas, Edurne; Bustince Sola, Humberto; Fernández Fernández, Francisco Javier; Galar Idoate, Mikel; Jurío Munárriz, Aránzazu; López Molina, Carlos; Paternain Dallo, Daniel; Sanz Delgado, José Antonio; Couto, Pedro; Melo-Pinto, Pedro; Automática y Computación; Automatika eta Konputazioa
This work presents a valuable review for the interested reader of the recent Works using extensions of fuzzy sets in image processing. The chapter is divided as follows: first we recall the basics of the extensions of fuzzy sets, i.e. Type 2 fuzzy sets, interval-valued fuzzy sets and Atanassov’s intuitionistic fuzzy sets. In sequent sections we review the methods proposed for noise removal (sections 3), image enhancement (section 4), edge detection (section 5) and segmentation (section 6). There exist other image segmentation tasks such as video de-interlacing, stereo matching or object representation that are not described in this work.
Open Access
A method for inference in approximate reasoning based on normal intuitionistic fuzzy sets
(Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 1995) Bustince Sola, Humberto; Burillo López, Pedro; Mohedano Salillas, Victoria; Automática y Computación; Automatika eta Konputazioa
This paper introduces a method of approximate inference which operates with nornal intuitionistic fuzzy. We give a definition of degree of compatibility between intuitionistic fuzzy sets and we present a method for the construction of these sets. Lastly we present the method of inference in approximate reasoning with normal intuitionistic fuzzy sets and we study its most immediate properties.
Open Access
A rule-based approach for interpretable intensity-modulated radiation therapy treatment selection
(IEEE, 2024-08-05) González García, Xabier; Fumanal Idocin, Javier; Nunez do Rio, Joan M.; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Artificial Intelligence (AI) methods are becoming essential in healthcare. In the context of Intensity-Modulated Radiation Therapy (IMRT), Knowledge-Based Planning (KBP) methodologies have enabled the modification of treatments in real-time to accommodate morphological changes in patients. KBP for IMRT is a data-driven approach that utilises real-time medical imaging to adjust the radiation dose for a patient as needed for the different stages of an illness. In this work we present an interpretable AI model that selects the best IMRT treatment alternatives and determines which is the best. We use an Adaptive Neuforuzzy Adaptive Inference System (ANFIS), which combines the potential of a neural network with the interpretability of a rule based system. We train the model in a supervised manner using the OpenKBP challenge data repository. For this purpose, we also developed a data augmentation method that is supported by Diffusion Probabilistic Models. This approach enables the generation of a wider spectrum of treatment qualities and aids regularisation. The primary advantage of this framework resides in its ability to offer explanations, which is essential in the deployment of medical procedures in real life. Moreover, it serves as a valuable means to test hypotheses concerning the quality of IMRT treatments. Our study reveals that the developed tool has substantial potential to establish itself as a reference in the realm of explainable IMRT treatment selection tools.
Open Access
Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface
(IEEE, 2019) Ko, Li-Wei; Lu, Yi-Chen; Bustince Sola, Humberto; Chang, Yu-Cheng; Chang, Yang; Fernández Fernández, Francisco Javier; Wang, Yu-Kai; Sanz Delgado, José Antonio; Pereira Dimuro, Graçaliz; Lin, Chin-Teng; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
Brain–computer interface technologies, such as steady-state visually evoked potential, P300, and motor imagery are methods of communication between the human brain and the external devices. Motor imagery–based brain–computer interfaces are popular because they avoid unnecessary external stimulus. Although feature extraction methods have been illustrated in several machine intelligent systems in motor imagery-based brain–computer interface studies, the performance remains unsatisfactory. There is increasing interest in the use of the fuzzy integrals, the Choquet and Sugeno integrals, that are appropriate for use in applications in which fusion of data must consider possible data interactions. To enhance the classification accuracy of brain-computer interfaces, we adopted fuzzy integrals, after employing the classification method of traditional brain–computer interfaces, to consider possible links between the data. Subsequently, we proposed a novel classification framework called the multimodal fuzzy fusion-based brain-computer interface system. Ten volunteers performed a motor imagery-based brain-computer interface experiment, and we acquired electroencephalography signals simultaneously. The multimodal fuzzy fusion-based brain-computer interface system enhanced performance compared with traditional brain–computer interface systems. Furthermore, when using the motor imagery-relevant electroencephalography frequency alpha and beta bands for the input features, the system achieved the highest accuracy, up to 78.81% and 78.45% with the Choquet and Sugeno integrals, respectively. Herein, we present a novel concept for enhancing brain–computer interface systems that adopts fuzzy integrals, especially in the fusion for classifying brain–computer interface commands.
Open Access
Uso de t-normas para el estudio de la convexidad en conjuntos difusos intervalo-valuados
(Universidad de Málaga, 2021) Huidobro, Pedro; Alonso, Pedro; Bustince Sola, Humberto; Janis, Vladimír; Montes Rodríguez, Susana; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
En muchos problemas reales no se pueden tomar medidas de forma exacta. Así, los conjuntos difusos surgieron como una forma de intentar tratar con la incertidumbre de la forma más eficiente posible. Por otro lado, debe señalarse que la ‘convexidad es un concepto interesante en varias áreas dentro de las matemáticas. Teniendo esto en cuenta, en este documento proponemos una extensión del concepto de convexidad para conjuntos difusos intervalo-valuados basada en el uso de t-normas para intervalos. Para ello, y teniendo en consideración la literatura científica existente respecto de t-normas, presentamos una definición de t-norma aplicada a intervalos. Por último, comprobamos que nuestra definición de convexidad, utilizando t-normas, preserva la convexidad a través de intersecciones, es decir, que la intersección de dos conjuntos difusos intervalo-valuados convexos es también convexa.
Open Access
Cuantificar los hechos represivos: explicación y retos de la base de datos del fondo documental de la memoria histórica en Navarra
(2019) Majuelo Gil, Emilio; Mendiola Gonzalo, Fernando; Garmendia Amutxastegi, Gotzon; Piérola Narvarte, Gemma; García Funes, Juan Carlos; Yániz Berrio, Edurne; Pérez Ibarrola, Nerea; Barrenechea Tartas, Edurne; Rodríguez Martínez, Iosu; Sesma Redín, Rubén; Bustince Sola, Humberto; Ciencias Humanas y de la Educación; Giza eta Hezkuntza Zientziak; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
En este documento se presentan los fundamentos historiográficos y metodológicos de la base de datos del Fondo Documental de la Memoria Histórica en Navarra, desarrollada en la Universidad Pública de Navarra como consecuencia del encargo institucional realizado por el Parlamento y el Gobierno de Navarra. Con este fin, se ha procedido a elaborar una base de datos que permita una ágil consulta por parte de diferentes agentes sociales, institucionales y académicos en torno a la represión franquista, intentando incluir en ella la gran variedad de prácticas represivas que la historiografía ha ido identificando. Primeramente, se presenta un balance sobre la publicación, en los últimos años, de diferentes bases de datos on-line en torno a las víctimas de la guerra civil y la represión franquista en varias comunidades autónomas. A continuación, se presenta la unidad de análisis de nuestra base de datos, “los hechos represivos”, insertándola en el contexto historiográfico en torno a la represión franquista y los estudios sobre la violencia. En un tercer apartado pasamos a describir las diferentes categorías y subcategorías represivas en las que se enmarcan los hechos represivos, y finalmente se presentan algunas características técnicas de la organización interna de la información y el sofware de la base de datos.
Open Access
Hyperspectral imaging using notions from type-2 fuzzy sets
(Springer, 2019) López Maestresalas, Ainara; Miguel Turullols, Laura de; López Molina, Carlos; Arazuri Garín, Silvia; Bustince Sola, Humberto; Jarén Ceballos, Carmen; Ingeniería; Ingeniaritza; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Fuzzy set theory has developed a prolific armamentarium of mathematical tools for each of the topics that has fallen within its scope. One of such topics is data comparison, for which a range of operators has been presented in the past. These operators can be used within the fuzzy set theory, but can also be ported to other scenarios in which data are provided in various representations. In this work, we elaborate on notions for type-2 fuzzy sets, specifically for the comparison of type-2 fuzzy membership degrees, to create function comparison operators. We further apply these operators to hyperspectral imaging, in which pixelwise data are provided as functions over a certain energy spectra. The performance of the functional comparison operators is put to the test in the context of in-laboratory hyperspectral image segmentation.
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.