• A Machine Learning Solution for Distributed Environments and Edge Computing 

      Penas-Noce, Javier; Fontenla-Romero, Óscar; Guijarro-Berdiñas, Bertha (MDPI AG, 2019-08-09)
      [Abstract] In a society in which information is a cornerstone the exploding of data is crucial. Thinking of the Internet of Things, we need systems able to learn from massive data and, at the same time, being inexpensive ...
    • Feature selection with limited bit depth mutual information for portable embedded systems 

      Morán-Fernández, Laura; Sechidis, Konstantinos; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo; Brown, Gavin (Elsevier, 2020-06)
      [Abstract]: Since wearable computing systems have grown in importance in the last years, there is an increased interest in implementing machine learning algorithms with reduced precision parameters/computations. Not only ...
    • Towards federated feature selection: Logarithmic division for resource-conscious methods 

      Suárez-Marcote, Samuel; Morán-Fernández, Laura; Bolón-Canedo, Verónica (Elsevier, 2024)
      [Abstract]: Feature selection is a popular preprocessing step to reduce the dimensionality of the data while preserving the important information. In this paper, we propose an efficient and green feature selection method ...