Mostrando ítems 26-30 de 90

    • Community detection and social network analysis based on the Italian wars of the 15th century 

      Fumanal-Idocin, Javier; Alonso-Betanzos, Amparo; Cordón, Oscar; Bustince, Humberto; Minárová, Mária (Elsevier, 2020)
      [Abstract]: In this contribution we study social network modelling by using human interaction as a basis. To do so, we propose a new set of functions, affinities, designed to capture the nature of the local interactions ...
    • Data-driven predictive maintenance framework for railway systems 

      Meira, Jorge; Veloso, Bruno; Bolón-Canedo, Verónica; Marreiros, Goreti; Alonso-Betanzos, Amparo; Gama, João (IOS Press, 2023)
      [Abstract]: The emergence of the Industry 4.0 trend brings automation and data exchange to industrial manufacturing. Using computational systems and IoT devices allows businesses to collect and deal with vast volumes of ...
    • Dealing with heterogeneity in the context of distributed feature selection for classification 

      Morillo-Salas, José Luis; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo (Springer, 2021)
      [Abstract]: Advances in the information technologies have greatly contributed to the advent of larger datasets. These datasets often come from distributed sites, but even so, their large size usually means they cannot be ...
    • Distributed classification based on distances between probability distributions in feature space 

      Montero Manso, Pablo; Morán-Fernández, Laura; Bolón-Canedo, Verónica; Vilar, José; Alonso-Betanzos, Amparo (Elsevier, 2019-09)
      [Abstract]: We consider a distributed framework where training and test samples drawn from the same distribution are available, with the training instances spread across disjoint nodes. In this setting, a novel learning ...
    • Ensembles for feature selection: A review and future trends 

      Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo (Elsevier, 2019)
      [Abstract]: Ensemble learning is a prolific field in Machine Learning since it is based on the assumption that combining the output of multiple models is better than using a single model, and it usually provides good ...