Open Access
Combinations of affinity functions for different community detection algorithms in social networks
(University of Hawaii Press, 2021) Fumanal Idocin, Javier; Cordón, Óscar; Minárová, María; Alonso Betanzos, Amparo; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Social network analysis is a popular discipline among the social and behavioural sciences, in which the relationships between different social entities are modelled as a network. One of the most popular problems in social network analysis is finding communities in its network structure. Usually, a community in a social network is a functional sub-partition of the graph. However, as the definition of community is somewhat imprecise, many algorithms have been proposed to solve this task, each of them focusing on different social characteristics of the actors and the communities. In this work we propose to use novel combinations of affinity functions, which are designed to capture different social mechanics in the network interactions. We use them to extend already existing community detection algorithms in order to combine the capacity of the affinity functions to model different social interactions than those exploited by the original algorithms.
Open Access
Sugeno integral generalization applied to improve adaptive image binarization
(Elsevier, 2021) Bardozzo, Francesco; Osa Hernández, Borja de la; Horanská, Lubomíra; Fumanal Idocin, Javier; Priscoli, Mattia delli; Troiano, Luigi; Tagliaferri, Roberto; Fernández Fernández, Francisco Javier; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Gobierno de Navarra / Nafarroako Gobernua, PI043-2019; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PC093-094 TFIPDL
Classic adaptive binarization methodologies threshold pixels intensity with respect to adjacent pixels exploiting integral images. In turn, integral images are generally computed optimally by using the summed-area-table algorithm (SAT). This document presents a new adaptive binarization technique based on fuzzy integral images. Which, in turn, this technique is supported by an efficient design of a modified SAT for generalized Sugeno fuzzy integrals. We define this methodology as FLAT (Fuzzy Local Adaptive Thresholding). Experimental results show that the proposed methodology produced a better image quality thresholding than well-known global and local thresholding algorithms. We proposed new generalizations of different fuzzy integrals to improve existing results and reaching an accuracy ≈0.94 on a wide dataset. Moreover, due to high performances, these new generalized Sugeno fuzzy integrals created ad hoc for adaptive binarization, can be used as tools for grayscale processing and more complex real-time thresholding applications.
Open Access
Gated local adaptive binarization using supervised learning
(CEUR Workshop Proceedings (CEUR-WS.org), 2021) Fumanal Idocin, Javier; Uriarte Barragán, Juan; Osa Hernández, Borja de la; Bardozzo, Francesco; Fernández Fernández, Francisco Javier; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Image thresholding is one of the most popular problems in image processing. However, changes inlightning and contrast in an image can cause trouble for the existing algorithms that use a global threshold for all the image. A solution for this problem is the adaptive thresholding, in which an image canhave different thresholds for different parts of the image. Yet, the problem of choosing the most suitable threshold for each region of the image is still open. In this paper we present the Gated Local Adaptive Binarization algorithm, in which we choose the most appropriate threshold for each region of the image using a logistic regression. Our results show that this algorithm can effectively learn the most appropriate threshold in each situation, and beats other adaptive binarization solutions for a standard dataset in the literature.
Open Access
Motor-imagery-based brain-computer interface using signal derivation and aggregation functions
(IEEE, 2021) Fumanal Idocin, Javier; Wang, Yu-Kai; Lin, Chin-Teng; Fernández Fernández, Francisco Javier; Sanz Delgado, José Antonio; Bustince Sola, Humberto; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
Brain Computer Interface (BCI) technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is Motor Imagery (MI). In BCI applications, the ElectroEncephaloGraphy (EEG) is a very popular measurement for brain dynamics because of its non-invasive nature. Although there is a high interest in the BCI topic, the performance of existing systems is still far from ideal, due to the difficulty of performing pattern recognition tasks in EEG signals. This difficulty lies in the selection of the correct EEG channels, the signal-tonoise ratio of these signals and how to discern the redundant information among them. BCI systems are composed of a wide range of components that perform signal pre-processing, feature extraction and decision making. In this paper, we define a new BCI Framework, named Enhanced Fusion Framework, where we propose three different ideas to improve the existing MI-based BCI frameworks. Firstly, we include an additional pre-processing step of the signal: a differentiation of the EEG signal that makes it time-invariant. Secondly, we add an additional frequency band as feature for the system: the Sensory Motor Rhythm band, and we show its effect on the performance of the system. Finally, we make a profound study of how to make the final decision in the system. We propose the usage of both up to six types of different classifiers and a wide range of aggregation functions (including classical aggregations, Choquet and Sugeno integrals and their extensions and overlap functions) to fuse the information given by the considered classifiers. We have tested this new system on a dataset of 20 volunteers performing motor imagery-based braincomputer interface experiments. On this dataset, the new system achieved a 88.80% of accuracy. We also propose an optimized version of our system that is able to obtain up to 90, 76%. Furthermore, we find that the pair Choquet/Sugeno integrals and overlap functions are the ones providing the best results.
Open Access
Supervised penalty-based aggregation applied to motor-imagery based brain-computer-interface
(Elsevier, 2024) Fumanal Idocin, Javier; Vidaurre Arbizu, Carmen; Fernández Fernández, Francisco Javier; Gómez Fernández, Marisol; Andreu-Pérez, Javier; Prasad, M.; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Institute of Smart Cities - ISC
In this paper we propose a new version of penalty-based aggregation functions, the Multi Cost Aggregation choosing functions (MCAs), in which the function to minimize is constructed using a convex combination of two relaxed versions of restricted equivalence and dissimilarity functions instead of a penalty function. We additionally suggest two different alternatives to train a MCA in a supervised classification task in order to adapt the aggregation to each vector of inputs. We apply the proposed MCA in a Motor Imagery-based Brain- Computer Interface (MI-BCI) system to improve its decision making phase. We also evaluate the classical aggregation with our new aggregation procedure in two publicly available datasets. We obtain an accuracy of 82.31% for a left vs. right hand in the Clinical BCI challenge (CBCIC) dataset, and a performance of 62.43% for the four-class case in the BCI Competition IV 2a dataset compared to a 82.15% and 60.56% using the arithmetic mean. Finally, we have also tested the goodness of our proposal against other MI-BCI systems, obtaining better results than those using other decision making schemes and Deep Learning on the same datasets.