Recent Submissions

  • Effectiveness of Virtual Reality-Based Interventions for Children and Adolescents with ADHD 

    Toledano-González, Abel; Romero Ayuso, Dulce María; Rodríguez Martínez, María del Carmen; Arroyo Castillo, Palma; Triviño-Juárez, José-Matías; González, Pascual; Ariza-Vega, Patrocinio; Pino Gonzalez, Antonio del; Segura Fragoso, Antonio (MDPI, 2021-01)
    Abstract: This review aims to evaluate the effectiveness of virtual reality-based interventions (VRbased interventions) on cognitive deficits in children with attention deficit hyperactivity disorder (ADHD). A systematic ...
  • Mixture-based probabilistic graphical models for the partial label ranking problem 

    Alfaro, Juan C.; Aledo, Juan A.; Gámez, José (2021-11)
    The Label Ranking problem consists in learning preference models from training datasets labeled with a ranking of class labels, and the goal is to predict a ranking for a given unlabeled instance. In this work, we focus ...
  • Face recognition by counter-propagation networks 

    Moreno García, Juan; Fernández Graciani, Miguel Ángel; Gómez Quesada, Francisco Javier; Fernández Caballero, Antonio (SIARP, 2000)
    The functionality of counter-propagation nets applied to face recognition is presented inthis paper. The chosen procedure basically transforms the image into a vector of numbers andpasses them to the net’s input layer. The ...
  • Learning decision trees for the partial label ranking problem 

    Alfaro, Juan C.; Aledo, Juan A.; Gámez, Jose (Wiley, 2020-10-16)
    The Label Ranking (LR) problem is a well‐known nonstandard supervised classification problem, the goal of which is to learn preference classifiers from data, mapping instances to rankings of the labels of the class variable. ...
  • Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem 

    González Rodrigo, Enrique; Alfaro, Juan C.; Aledo, Juan A.; Gámez, José (MDPI, 2021-03)
    The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted ...
  • SLA-Driven modeling and verifying cloud systems: A Bigraphical reactive systems-based approach 

    Kamel, Oussama; Chaoui, Allaoua; Diaz, Gregorio; Gharzouli, Mohamed (Elsevier, 2021-02)
    We propose a formal approach based on Bigraphical Reactive Systems (BRSs) and model checking techniques for modeling and verifying the interaction behaviours of SLA-based cloud computing systems. In the first phase of this ...
  • A Compositional Approach for Complex Event Pattern Modeling and Transformation to Colored Petri Nets with Black Sequencing Transitions 

    Valero, Valentín; Diaz, Gregorio; Boubeta-Puig, Juan; Maciâ, Hermenegilda; Brazález, Enrique (Institute of Electrical and Electronics Engineers, 2021-03)
    Prioritized Colored Petri Nets (PCPNs) are a well-known extension of plain Petri nets in which transitions can have priorities and the tokens on the places carry data information. In this paper, we propose an extension of ...
  • Approaching rank aggregation problems by using evolution strategies: The case of the optimal bucket order problem 

    Aledo, Juan A.; Gámez, Jose; Rosete-Suárez, Alejandro (Elsevier, 2018-11)
    The optimal bucket order problem consists in obtaining a complete consensus ranking (ties are allowed) from a matrix of preferences (possibly obtained from a database of rankings). In this paper, we tackle this problem by ...
  • Tackling the supervised label ranking problem by bagging weak learners 

    Aledo, Juan A.; Gámez, Jose; Molina-García, David (Elsevier, 2017)
    Preference learning is the branch of machine learning in charge of inducing preference models from data. In this paper we focus on the task known as label ranking problem, whose goal is to predict a ranking among the ...
  • Approaching the Rank Aggregation Problem by Local Search-based Metaheuristics 

    Aledo, Juan A.; Gámez, Jose; Molina-García, David (Elsevier, 2019-07)
    Encouraged by the success of applying metaheuristics algorithms to other ranking-based problems (Kemeny ranking problem and pa rameter estimation for Mallows distributions), in this paper we deal with the rank aggregation ...
  • A Metahierarchical Rule Decision System to Design Robust Fuzzy Classifiers Based on Data Complexity 

    Cozar del Olmo, Javier; Fernandez Hilario, Alberto; Herrera, Francisco; Gámez, Jose (Institute of Electrical and Electronics Engineers, 2019-04)
    There is a wide variety of studies that propose different classifiers to solve a large amount of problems in distinct classification scenarios. The no free lunch theorem states that if we use a big enough set of varied ...
  • Adapting the CMIM algorithm for multi-label feature selection. A comparison with existing methods 

    Bermejo, Pablo; Gámez, Jose; Puerta, Jose M. (Wiley, 2018-02-18)
    The multi-label paradigm has recently attracted the attention of the machine learning community, multi-label problems being those which do not have only one class but several binomial classes instead. Although intensive ...
  • Assessment of flood risk in Mediterranean catchments: An approach based on Bayesian Networks 

    Flores, M. Julia; Rosa, F. Ropero; Rumi, Rafael (Springer, 2019-11)
    National and international technical reports have demonstrated the increase of extreme event occurrences which becomes more dangerous in coastal areas due to their higher population density. In Spain, flood and storm ...
  • On the use of local search heuristics to improve GES-based Bayesian network learning 

    Alonso, Juan I.; Ossa, Luis de la; Gámez, Jose; Puerta, Jose M. (Elsevier, 2018)
    Bayesian networks learning is computationally expensive even in the case of sacrificing the optimality of the result. Many methods aim at obtaining quality solutions in affordable times. Most of them are based on local ...
  • Machine learning from crowds a systematic review of its applications 

    González Rodrigo, Enrique; Aledo, Juan A.; Gámez, Jose (John Wiley & Sons, 2018-10)
    Crowdsourcing opens the door to solving a wide variety of problems that previ-ously were unfeasible in the field of machine learning, allowing us to obtain rela-tively low cost labeled data in a small amount of time. ...
  • Scaling up the learning-from-crowds GLAD algorithm using instance-difficulty clustering 

    González Rodrigo, Enrique; Aledo, Juan A.; Gámez, Jose (Springer, 2019-05)
    The main goal of this article is to improve the results obtained by the GLAD algorithm in cases with large data. This algorithm is able to learn from instances labeled by multiple annotators taking into account both the ...
  • Integration of Contextual Information into the Scene Classification Problem 

    Rubio Perona, Fernando; Martinez-Gomez, Jesus; Flores, M. Julia; Puerta, Jose M. (Elsevier, 2017-11)
    The task of identifying the semantic localization of a robot has commonly been treated as a classification problem, where images are taken as input and a set of predefined labels is the output. While traditional approaches ...
  • Global Software Development governance: Challenges and solutions 

    Manjavacas, Antonio; Vizcaino, Aurora; Ruiz, Francisco; Piattini, Mario (Wiley, 2020-04)
    Global software development (GSD) has become a rising software developmentmodel in the last few years. Although much research has been performed in terms ofGSD management, GSD governance research is scarce at the present ...
  • Measuring data credibility and medical coding: 

    Souza, Julio; Pimenta, Diana; Caballero, Ismael; Freitas, Alberto (Springer, 2020-07-12)
    Some countries have adopted the diagnosis-related groups (DRG) system to pay hospitals according to the number and complexity of patients they treat. Translating diseases and procedures into medical codes based on international ...
  • Multimodal Affective Computing to Enhance the User Experience of Educational Software Applications 

    García-García, José María; Penichet, Víctor M. R.; Lozano, María Dolores; Garrido, Juan Enrique; Lai-Chong Law, Effie (Hindawi, 2018-09-13)
    Affective computing is becoming more and more important as it enables to extend the possibilities of computing technologies by incorporating emotions. In fact, the detection of users’ emotions has become one of the most ...

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