Open Access
A study of different families of fusion functions for combining classifiers in the one-vs-one strategy
(Springer, 2018) Uriz Martín, Mikel Xabier; Paternain Dallo, Daniel; Jurío Munárriz, Aránzazu; Bustince Sola, Humberto; Galar Idoate, Mikel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
In this work we study the usage of different families of fusion functions for combining classifiers in a multiple classifier system of One-vs-One (OVO) classifiers. OVO is a decomposition strategy used to deal with multi-class classification problems, where the original multi-class problem is divided into as many problems as pair of classes. In a multiple classifier system, classifiers coming from different paradigms such as support vector machines, rule induction algorithms or decision trees are combined. In the literature, several works have addressed the usage of classifier selection methods for these kinds of systems, where the best classifier for each pair of classes is selected. In this work, we look at the problem from a different perspective aiming at analyzing the behavior of different families of fusion functions to combine the classifiers. In fact, a multiple classifier system of OVO classifiers can be seen as a multi-expert decision making problem. In this context, for the fusion functions depending on weights or fuzzy measures, we propose to obtain these parameters from data. Backed-up by a thorough experimental analysis we show that the fusion function to be considered is a key factor in the system. Moreover, those based on weights or fuzzy measures can allow one to better model the aggregation problem.
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
Speeding-up diffusion models for remote sensing semantic segmentation
(Elsevier, 2025-08-01) Pascual Casas, Rubén; Ayala Lauroba, Christian; Sesma Redín, Rubén; Jurío Munárriz, Aránzazu; Paternain Dallo, Daniel; Galar Idoate, Mikel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated exceptional potential across various generative modeling tasks. Despite evident promise in semantic segmentation, their adoption for remote sensing remains limited primarily due to computationally demanding inference. While initial approaches using DDPMs in remote sensing achieve competitive accuracy with state-of-the-art models, the multi-step nature of their image generation process poses a major bottleneck. To address this limitation, this paper investigates three key strategies for accelerating inference: (1) optimizing training and inference steps, (2) applying DDPM acceleration techniques adapted to segmentation task (including Denoising Diffusion Implicit Models, Improved Denoising Diffusion Models, and Progressive Distillation), and (3) thoroughly analyzing the trade-off between accuracy improvement and additional inference time when using test-time augmentation. These strategies are extensively tested with two established remote sensing semantic segmentation datasets focused on buildings and roads. Finally, we compare the optimized diffusion-based model with state-of-the-art convolutional-based models in terms of accuracy and inference times, showing the narrowing gap between both approaches and the increasing viability of diffusion-based segmentation for practical applications.
Open Access
Diseño y captura de una base de datos para el reconocimiento de emociones minimizando sesgos
(CAEPIA, 2024) Jurío Munárriz, Aránzazu; Pascual Casas, Rubén; Domínguez Catena, Iris; Paternain Dallo, Daniel; Galar Idoate, Mikel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertistate Publikoa; Gobierno de Navarra / Nafarroako Gobernua
El reconocimiento de emociones a partir de expresiones faciales (FER) es un campo de investigación importante para la interacción persona-máquina. Sin embargo, los conjuntos de datos utilizados para entrenar modelos FER a menudo contienen sesgos demográficos que pueden conducir a la discriminación en el modelo final. En este trabajo, presentamos el diseño y la captura realizados para la creación de una nueva base de datos para FER, donde tratamos de minimizar los sesgos desde el propio diseño. La base de datos se ha creado utilizando diferentes métodos de captura. Para comprobar la reducción de los sesgos alcanzada, analizamos diferentes métricas de sesgo representacional y estereotípico sobre la base de datos generada y la comparamos frente a otras bases de datos estándar en la literatura de FER.
Restricted
Sistema multimedia para el autoaprendizaje de la programación. Subsistema de adaptación web
(2006) Jurío Munárriz, Aránzazu; Garitagoitia Padrones, José Ramón; Pina Calafi, Alfredo; Escuela Técnica Superior de Ingenieros Industriales y de Telecomunicación; Telekomunikazio eta Industria Ingeniarien Goi Mailako Eskola Teknikoa; Ingeniería Matemática e Informática; Matematika eta Informatika Ingeniaritza
Open Access
Initiative to increment the number of women in STEM degrees: women, science and technology chair of the Public University of Navarre
(IEEE, 2020) Aranguren Garacochea, Patricia; San Martín Biurrun, Idoia; Catalán Ros, Leyre; Martínez Ramírez, Alicia; Jurío Munárriz, Aránzazu; Díaz Lucas, Silvia; Pérez Artieda, Miren Gurutze; Gómez Fernández, Marisol; Barrenechea Tartas, Edurne; Estadística, Informática y Matemáticas; Ingeniería; Ingeniería Eléctrica, Electrónica y de Comunicación; Estatistika, Informatika eta Matematika; Ingeniaritza; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Gobierno de Navarra / Nafarroako Gobernua; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
The Public University of Navarre joined with Navarre Government has created the Women, Science and Technology Chair. This chair arises due to the plummeting tendency of the percentage of women in STEM degrees with the aim of reversing this trend. The programme of activities is defined throughout this contribution by six activities: a Theatre Play, a Poster Award on Final Degree/Masters Project, The 1st Week of Women, Science and Technology, the Promotion of Technical Degrees in schools and high-schools, a Workshop about Gender Stereotypes and the Fostering of Women among Science and Environment. Each activity gained great success and the preset goals were highly accomplished, especially, the 1st Week of Women, Science and Technology activity. The latter achieved a great success both in participation and in repercussion, contributing to visualize the role of women in science and technology.
Open Access
Evolution in time of L-fuzzy context sequences
(Elsevier, 2016) Alcalde, Cristina; Burusco Juandeaburre, Ana; Bustince Sola, Humberto; Jurío Munárriz, Aránzazu; Sanz Delgado, José Antonio; Automatika eta Konputazioa; Institute of Smart Cities - ISC; Automática y Computación; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
In this work, we consider a complete lattice L and we study L-fuzzy context sequences which represent the evolution in time of an L-fuzzy context. To carry out this study, in the first part of the paper, we consider n-ary OWA operators in complete lattices, which enable us to make a general analysis and a temporal analysis at any moment in time of L-fuzzy context sequences. After that, evolution in time of the relationship between the objects and the attributes is considered. In particular, we analyze the concepts of Trend and Persistent formal contexts. Finally, we illustrate our results with an example where we consider the particular lattice L = J ([0, 1]).
Open Access
"Yo quiero ser científica": a creative way to inspire girls in science
(IEEE, 2020-06-25) Jurío Munárriz, Aránzazu; Catalán Ros, Leyre; San Martín Biurrun, Idoia; Aranguren Garacochea, Patricia; Martínez Ramírez, Alicia; Díaz Lucas, Silvia; Barrenechea Tartas, Edurne; Gómez Fernández, Marisol; Pérez Artieda, Miren Gurutze; Ingeniería; Ingeniaritza; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute for Advanced Materials and Mathematics - INAMAT2; Institute of Smart Cities - ISC
The number of women in scientific and technological careers is quite small compared to the number of men in the same careers. To equalize these numbers, the Public University of Navarra started a new initiative in 2017. The goal of this project is to make more-women in STEM visible, in order to serve as example for the young girls. It is brought to life by means of a theatre play called “Yo quiero ser científica” (translated as “I want to be a female scientist”). This play is divided into two parts: in the first one 9 teachers perform the role of 9 historical female scientists, by means of 9 monologues; in the second part, the same 9 teachers tell their experience as contemporary scientists. The feedback obtained through surveys, a contest and a final debate with the attendees show that this kind on initiatives are really necessary.
Open Access
Type-2 fuzzy entropy-sets
(IEEE, 2017) Miguel Turullols, Laura de; Santos, Helida; Sesma Sara, Mikel; Bedregal, Benjamin; Jurío Munárriz, Aránzazu; Bustince Sola, Humberto; Automática y Computación; Automatika eta Konputazioa; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
The final goal of this study is to adapt the concept of fuzzy entropy of De Luca and Termini to deal with Type-2 Fuzzy Sets. We denote this concept Type-2 Fuzzy Entropy-Set. However, the construction of the notion of entropy measure on an infinite set, such us [0, 1], is not effortless. For this reason, we first introduce the concept of quasi-entropy of a Fuzzy Set on the universe [0, 1]. Furthermore, whenever the membership function of the considered Fuzzy Set in the universe [0, 1] is continuous, we prove that the quasi-entropy of that set is a fuzzy entropy in the sense of De Luca y Termini. Finally, we present an illustrative example where we use Type-2 Fuzzy Entropy-Sets instead of fuzzy entropies in a classical fuzzy algorithm.
Open Access
Less can be more: representational vs. stereotypical gender bias in facial expression recognition
(Springer, 2024-10-14) Domínguez Catena, Iris; Paternain Dallo, Daniel; Jurío Munárriz, Aránzazu; Galar Idoate, Mikel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Universidad Publica de Navarra / Nafarroako Unibertsitate Publikoa
Machine learning models can inherit biases from their training data, leading to discriminatory or inaccurate predictions. This is particularly concerning with the increasing use of large, unsupervised datasets for training foundational models. Traditionally, demographic biases within these datasets have not been well-understood, limiting our ability to understand how they propagate to the models themselves. To address this issue, this paper investigates the propagation of demographic biases from datasets into machine learning models. We focus on the gender demographic component, analyzing two types of bias: representational and stereotypical. For our analysis, we consider the domain of facial expression recognition (FER), a field known to exhibit biases in most popular datasets. We use Affectnet, one of the largest FER datasets, as our baseline for carefully designing and generating subsets that incorporate varying strengths of both representational and stereotypical bias. Subsequently, we train several models on these biased subsets, evaluating their performance on a common test set to assess the propagation of bias into the models¿ predictions. Our results show that representational bias has a weaker impact than expected. Models exhibit a good generalization ability even in the absence of one gender in the training dataset. Conversely, stereotypical bias has a significantly stronger impact, primarily concentrated on the biased class, although it can also influence predictions for unbiased classes. These results highlight the need for a bias analysis that differentiates between types of bias, which is crucial for the development of effective bias mitigation strategies.