Artículos de revista DAC - AKS Aldizkari artikuluak

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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
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
Analysis and stochastic characterization of TCP flows
(Springer, 2000) Aracil Rico, Javier; Morató Osés, Daniel; Izal Azcárate, Mikel; Automática y Computación; Automatika eta Konputazioa
Since the most Internet services use TCP as a transport protocol there is a growing interest in the characterization of TCP flows. However, the flow characteristics depend on a large number of factors, due to the complexity of the TCP. As a result, the TCS characteristics are normally studies by means of simulations or controlled network setups. In this paper we propose a TCP characterization based on a generic model based of stochastic flow with burstiness and throughput (((σ, ρ)-constraints), which is useful in order to characterize flows in ATM and other flow-switched networks. The model is obtained through extensive analysis of a real traffic trace, comprising an approximate number of 1,500 hosts and 1,700,000 TCP connections. The results suggests that TCP connections in the wide area Internet have low throughput while the packet bursts do not suffer an exponential increase, as indicated by the slow-start behavior. On the other hand, the impact of the connection establishment phase is striking. We note that the throughput of the TCP flow is approximately half the throughput which is obtained in the data transfer phase, namely after the connection has been established.
Open Access
Application of the L-fuzzy concept analysis in the morphological image and signal processing
(Springer International Publishing, 2014) Alcalde, Cristina; Burusco Juandeaburre, Ana; Fuentes González, Ramón; Automática y Computación; Automatika eta Konputazioa; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
In this work we are going to set up a new relationship between the L-fuzzy Concept Analysis and the Fuzzy Mathematical Morphology. Specifically we prove that the problem of finding fuzzy images or signals that remain invariant under a fuzzy morphological opening or under a fuzzy morphological closing, is equal to the problem of finding the L-fuzzy concepts of some L-fuzzy context. Moreover, since the Formal Concept Analysis and the Mathematical Morphology are the particular cases of the fuzzy ones, the showed result has also an interpretation for binary images or signals.
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.; 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.
Embargo
Binary relations coming from solutions of functional equations: orderings and fuzzy subsets
(World Scientific Publishing Company, 2017) Campión Arrastia, María Jesús; Miguel Turullols, Laura de; García Catalán, Olga Raquel; Induráin Eraso, Esteban; Abrísqueta Usaola, Francisco Javier; Automatika eta Konputazioa; Matematika; Institute of Smart Cities - ISC; Institute for Advanced Research in Business and Economics - INARBE; Institute for Advanced Materials and Mathematics - INAMAT2; Automática y Computación; Matemáticas; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
We analyze the main properties of binary relations, defined on a nonempty set, that arise in a natural way when dealing with real-valued functions that satisfy certain classical functional equations on two variables. We also consider the converse setting, namely, given binary relations that accomplish some typical properties, we study whether or not they come from solutions of some functional equation. Applications to the numerical representability theory of ordered structures are also furnished as a by-product. Further interpretations of this approach as well as possible generalizations to the fuzzy setting are also commented. In particular, we discuss how the values taken for bivariate functions that are bounded solutions of some classical functional equations define, in a natural way, fuzzy binary relations on a set.
Open Access
BioBuilder as a database development and functional annotation platform for proteins
(BioMed Central, 2004) Navarro, J. Daniel; Talreja, Naveen; Peri, Suraj; Vrushabendra, B. M.; Rashmi, B. P.; Padma, N.; Surendranath, Vineeth; Jonnalagadda, Chandra Kiran; Kousthub, P. S.; Deshpande, Nandan; Shanker, K.; Pandey, Akhilesh; Automática y Computación; Automatika eta Konputazioa
Background: The explosion in biological information creates the need for databases that are easy to develop, easy to maintain and can be easily manipulated by annotators who are most likely to be biologists. However, deployment of scalable and extensible databases is not an easy task and generally requires substantial expertise in database development. Results: BioBuilder is a Zope-based software tool that was developed to facilitate intuitive creation of protein databases. Protein data can be entered and annotated through web forms along with the flexibility to add customized annotation features to protein entries. A built-in review system permits a global team of scientists to coordinate their annotation efforts. We have already used BioBuilder to develop Human Protein Reference Database http://www.hprd.org, a comprehensive annotated repository of the human proteome. The data can be exported in the extensible markup language (XML) format, which is rapidly becoming as the standard format for data exchange. Conclusions: As the proteomic data for several organisms begins to accumulate, BioBuilder will prove to be an invaluable platform for functional annotation and development of customizable protein centric databases. BioBuilder is open source and is available under the terms of LGPL
Open Access
A compact evolutionary interval-valued fuzzy rule-based classification system for the modeling and prediction of real-world financial applications with imbalanced data
(IEEE, 2014) Sanz Delgado, José Antonio; Bernardo, Darío; Herrera, Francisco; Bustince Sola, Humberto; Hagras, Hani; Automática y Computación; Automatika eta Konputazioa
The current financial crisis has stressed the need of obtaining more accurate prediction models in order to decrease the risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle the real-world imbalanced financial data sets without using sampling techniques which might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on IVTURSFARC-HD (Interval-Valued fuzzy rule-based classification system with TUning and Rule Selection) [22]), for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good predictions accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and thus avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including eleven real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1 and interval-valued fuzzy counterparts which use the SMOTE sampling technique to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost sensitive C4.5 and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids pre-processing techniques and it provides interpretable models that allow obtaining more accurate results.
Open Access
Concept lattices associated with interval-valued L-fuzzy contexts
(Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 1995) Burusco Juandeaburre, Ana; Fuentes González, Ramón; Automática y Computación; Automatika eta Konputazioa
In 1882 Wille published the paper titled Restructuring lattice theory: an approach based on hierarchies of concepts, where he developed a new theory called formal concept theory. In that paper he analyzes 0/1 relations between the object and attribute sets. Applying the L-fuzzy set theory to Wille’s results, we have developed the L-fuzzy concept theory dealing with L-fuzzy relations inside 0/1 relations. The final purpose of our theory is knowledge acquisition and classification. Taking as departure point the L-fuzzy concept theory, in this paper we study contexts defined by interval-valued L-fuzzy relations between the object and attribute sets. We give a new definition of L-fuzzy concept valid for this case and analyze the structure of the L-fuzzy concept set. We apply these results to a medical example.
Open Access
Consensus image method for unknown noise removal
(Elsevier, 2014) González Jaime, Luis; Kerre, Etienne E.; Nachtegael, Mike; Bustince Sola, Humberto; Automática y Computación; Automatika eta Konputazioa
Noise removal has been, and it is nowadays, an important task in computer vision. Usually, it is a previous task preceding other tasks, as segmentation or reconstruction. However, for most existing denoising algorithms the noise model has to be known in advance. In this paper, we introduce a new approach based on consensus to deal with unknown noise models. To do this, different filtered images are obtained, then combined using multifuzzy sets and averaging aggregation functions. The final decision is made by using a penalty function to deliver the compromised image. Results show that this approach is consistent and provides a good compromise between filters.
Open Access
Construction of admissible linear orders for interval-valued Atanassov intuitionistic fuzzy sets with an application to decision making
(Elsevier, 2015) Miguel Turullols, Laura de; Bustince Sola, Humberto; Fernández Fernández, Francisco Javier; Induráin Eraso, Esteban; Kolesárová, Anna; Mesiar, Radko; Matemáticas; Matematika; Automática y Computación; Automatika eta Konputazioa; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
In this work we introduce a method for constructing linear orders between pairs of intervals by using aggregation functions. We adapt this method to the case of interval-valued Atanassov intuitionistic fuzzy sets and we apply these sets and the considered orders to a decision making problem.
Open Access
Convolution lattices
(Elsevier, 2018) Miguel Turullols, Laura de; Bustince Sola, Humberto; Baets, Bernard de; Automatika eta Konputazioa; Institute of Smart Cities - ISC; Automática y Computación
We propose two convolution operations on the set of functions between two bounded lattices and investigate the algebraic structure they constitute, in particular the lattice laws they satisfy. Each of these laws requires the restriction to a specific subset of functions, such as normal, idempotent or convex functions. Combining all individual results, we identify the maximal subsets of functions resulting in a bounded lattice, and show this result to be equivalent to the distributivity of the lattice acting as domain of the functions. Furthermore, these lattices turn out to be distributive as well. Additionally, we show that for the larger subset of idempotent functions, although not satisfying the absorption laws, the convolution operations satisfy the Birkhoff equation.
Open Access
d-XC integrals: on the generalization of the expanded form of the Choquet integral by restricted dissimilarity functions and their applications
(IEEE, 2022) Wieczynski, Jonata; Fumanal Idocin, Javier; Lucca, Giancarlo; Borges, Eduardo N.; Da Cruz Asmus, Tiago; Emmendorfer, Leonardo R.; Bustince Sola, Humberto; Pereira Dimuro, Graçaliz; Automática y Computación; Automatika eta Konputazioa; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Restricted dissimilarity functions (RDFs) were introduced to overcome problems resulting from the adoption of the standard difference. Based on those RDFs, Bustince et al. introduced a generalization of the Choquet integral (CI), called d-Choquet integral, where the authors replaced standard differences with RDFs, providing interesting theoretical results. Motivated by such worthy properties, joint with the excellent performance in applications of other generalizations of the CI (using its expanded form, mainly), this paper introduces a generalization of the expanded form of the standard Choquet integral (X-CI) based on RDFs, which we named d-XC integrals. We present not only relevant theoretical results but also two examples of applications. We apply d-XC integrals in two problems in decision making, namely a supplier selection problem (which is a multi-criteria decision making problem) and a classification problem in signal processing, based on motor-imagery brain-computer interface (MI-BCI). We found that two d-XC integrals provided better results when compared to the original CI in the supplier selection problem. Besides that, one of the d-XC integrals performed better than any previous MI-BCI results obtained with this framework in the considered signal processing problem.
Open Access
A decision tree based approach with sampling techniques to predict the survival status of poly-trauma patients
(Atlantis Press, 2017) Sanz Delgado, José Antonio; Fernández Fernández, Francisco Javier; Bustince Sola, Humberto; Gradín Purroy, Carlos; Belzunegui Otano, Tomás; Automatika eta Konputazioa; Osasun Zientziak; Institute of Smart Cities - ISC; Automática y Computación; Ciencias de la Salud; Gobierno de Navarra / Nafarroako Gobernua, PI-019/11
Survival prediction of poly-trauma patients measure the quality of emergency services by comparing their predictions with the real outcomes. The aim of this paper is to tackle this problem applying C4.5 since it achieves accurate results and it provides interpretable models. Furthermore, we use sampling techniques because, among the 378 patients treated at the Hospital of Navarre, the number of survivals excels that of deaths. Logistic regressions are used in the comparison, since they are an standard in this domain.
Open Access
Delay-throughput curves for timer-based OBS burstifiers with light load
(IEEE, 2006) Izal Azcárate, Mikel; Aracil Rico, Javier; Morató Osés, Daniel; Magaña Lizarrondo, Eduardo; Automática y Computación; Automatika eta Konputazioa
The OBS burstifier delay-throughput curves are analyzed in this paper. The burstifier incorporates a timer-based scheme with minimum burst size, i. e., bursts are subject to padding in light-load scenarios. Precisely, due to this padding effect, the burstifier normalized throughput may not be equal to unity. Conversely, in a high-load scenario, padding will seldom occur. For the interesting light-load scenario, the throughput delay curves are derived and the obtained results are assessed against those obtained by trace-driven simulation. The influence of long-range dependence and instantaneous variability is analyzed to conclude that there is a threshold timeout value that makes the throughput curves flatten out to unity. This result motivates the introduction of adaptive burstification algorithms, that provide a timeout value that minimizes delay, yet keeping the throughput very close to unity. The dependence of such optimum timeout value with traffic long-range dependence and instantaneous burstiness is discussed. Finally, three different adaptive timeout algorithms are proposed, that tradeoff complexity versus accuracy.
Open Access
Desarrollo de un sistema de decisión basado en lógica borrosa para el uso de bombas de insulina
(AISTI - Associação Ibérica de Sistemas e Tecnologias de Informação, 2014) Galván Herrera, José Basilio; Recarte Mendiburu, Leticia; Pérez-Ilzarbe Serrano, María José; Automática y Computación; Automatika eta Konputazioa
Basándonos en el conocimiento de un usuario experto en la utilización de bombas de insulina, hemos desarrollado un software de apoyo para usuarios noveles de dicha tecnología. Nuestro sistema de decisión tiene en cuenta problemas tales como la influencia de las características específicas de cada usuario, el ritmo circadiano, la actividad prevista y la glucemia preprandial. La base de nuestro sistema es un controlador borroso para el que se ha desarrollado un constructor semiautomático de reglas. El sistema ha sido probado con éxito por dos usuarios muy diferentes entre sí, lo que prueba que las ideas implementadas son válidas y suponen una interesante línea de desarrollo de sistemas de ayuda a pacientes diabéticos.
Open Access
Descubrimiento de conocimiento en bases de datos utilizando técnicas de morfología matemática borrosa
(Centro de Información Tecnológica, 2007) Frago Paños, Noé Natalio; Fuentes González, Ramón; Automática y Computación; Automatika eta Konputazioa
En este artículo se analiza el efecto, la utilidad y la interpretación de los filtros asociados a la Morfología Matemática Borrosa en procesos de Descubrimiento de Conocimiento en Bases de Datos. En particular se estudian los operadores morfológicos erosión, dilatación, apertura, cierre, Top-Hat y Hit-or-Miss. Usando información de las bases de datos y relaciones binarias ordinarias como elementos estructurales, se implementan algunos de estos filtros. Con esta implementación se justifica que, con estos filtros morfológicos, pueden analizarse datos estructurados como tabla de registros, obteniéndose información útil no evidente. Finalmente se analizan diversas características de los operadores definidos, ilustrando los resultados con un ejemplo.
Open Access
Enhancing multi-class classification in FARC-HD fuzzy classifier: on the synergy between n-dimensional overlap functions and decomposition strategies
(IEEE, 2014) Elkano Ilintxeta, Mikel; Galar Idoate, Mikel; Sanz Delgado, José Antonio; Fernández, Alberto; Barrenechea Tartas, Edurne; Herrera, Francisco; Bustince Sola, Humberto; Automática y Computación; Automatika eta Konputazioa
There are many real-world classification problems involving multiple classes, e.g., in bioinformatics, computer vision or medicine. These problems are generally more difficult than their binary counterparts. In this scenario, decomposition strategies usually improve the performance of classifiers. Hence, in this paper we aim to improve the behaviour of FARC-HD fuzzy classifier in multi-class classification problems using decomposition strategies, and more specifically One-vs-One (OVO) and One-vs-All (OVA) strategies. However, when these strategies are applied on FARC-HD a problem emerges due to the low confidence values provided by the fuzzy reasoning method. This undesirable condition comes from the application of the product t-norm when computing the matching and association degrees, obtaining low values, which are also dependent on the number of antecedents of the fuzzy rules. As a result, robust aggregation strategies in OVO such as the weighted voting obtain poor results with this fuzzy classifier. In order to solve these problems, we propose to adapt the inference system of FARC-HD replacing the product t-norm with overlap functions. To do so, we define n-dimensional overlap functions. The usage of these new functions allows one to obtain more adequate outputs from the base classifiers for the subsequent aggregation in OVO and OVA schemes. Furthermore, we propose a new aggregation strategy for OVO to deal with the problem of the weighted voting derived from the inappropriate confidences provided by FARC-HD for this aggregation method. The quality of our new approach is analyzed using twenty datasets and the conclusions are supported by a proper statistical analysis. In order to check the usefulness of our proposal, we carry out a comparison against some of the state-of-the-art fuzzy classifiers. Experimental results show the competitiveness of our method.
Open Access
EUSC: a clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification
(Elsevier, 2021) Le, Hoang Lam; Landa-Silva, Darío; Galar Idoate, Mikel; García, Salvador; Triguero, Isaac; Automática y Computación; Automatika eta Konputazioa
Learning from imbalanced datasets is highly demanded in real-world applications and a challenge for standard classifiers that tend to be biased towards the classes with the majority of the examples. Undersampling approaches reduce the size of the majority class to balance the class distributions. Evolutionary-based approaches are prominent, treating undersampling as a binary optimisation problem that determines which examples are removed. However, their utilisation is limited to small datasets due to fitness evaluation costs. This work proposes a two-stage clustering-based surrogate model that enables evolutionary undersampling to compute fitness values faster. The main novelty lies in the development of a surrogate model for binary optimisation which is based on the meaning (phenotype) rather than their binary representation (genotype). We conduct an evaluation on 44 imbalanced datasets, showing that in comparison with the original evolutionary undersampling, we can save up to 83% of the runtime without significantly deteriorating the classification performance.