Listar por autor "Bolón-Canedo, Verónica"

Mostrando ítems 21-28 de 28

    • Machine Learning Techniques to Predict Different Levels of Hospital Care of CoVid-19 

      Hernández-Pereira, Elena; Fontenla-Romero, Óscar; Bolón-Canedo, Verónica; Cancela, Brais; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo (Springer, 2022)
      [Abstract] In this study, we analyze the capability of several state of the art machine learning methods to predict whether patients diagnosed with CoVid-19 (CoronaVirus disease 2019) will need different levels of hospital ...

    • Multithreaded and Spark parallelization of feature selection filters 

      Eiras-Franco, Carlos; Bolón-Canedo, Verónica; Ramos Garea, Sabela; González-Domínguez, Jorge; Alonso-Betanzos, Amparo; Touriño, Juan (2016)
      [Abstract]: Vast amounts of data are generated every day, constituting a volume that is challenging to analyze. Techniques such as feature selection are advisable when tackling large datasets. Among the tools that provide ...

    • Novel feature selection methods for high dimensional data 

      Bolón-Canedo, Verónica (2014)
      [Resumen] La selección de características se define como el proceso de detectar las características relevantes y descartar las irrelevantes, con el objetivo de obtener un subconjunto de características más pequeño que ...

    • On developing an automatic threshold applied to feature selection ensembles 

      Seijo Pardo, Borja; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo (Elsevier, 2019-01)
      [Abstract]: Feature selection ensemble methods are a recent approach aiming at adding diversity in sets of selected features, improving performance and obtaining more robust and stable results. However, using an ensemble ...

    • On the Effectiveness of Convolutional Autoencoders on Image-Based Personalized Recommender Systems 

      Blanco, Eva; Remeseiro, Beatriz; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo (MDPI AG, 2020-08-19)
      [Abstract] Over the years, the success of recommender systems has become remarkable. Due to the massive arrival of options that a consumer can have at his/her reach, a collaborative environment was generated, where users ...

    • On the scalability of feature selection methods on high-dimensional data 

      Bolón-Canedo, Verónica; Rego-Fernández, Diego; Peteiro Barral, Diego; Alonso-Betanzos, Amparo; Guijarro-Berdiñas, Bertha; Sánchez-Maroño, Noelia (Springer, 2018)
      [Abstract]: Lately, derived from the explosion of high dimensionality, researchers in machine learning became interested not only in accuracy, but also in scalability. Although scalability of learning methods is a trending ...

    • Parallel feature selection for distributed-memory clusters 

      González-Domínguez, Jorge; Bolón-Canedo, Verónica; Freire, Borja; Touriño, Juan (2019)
      [Abstract]: Feature selection is nowadays an extremely important data mining stage in the field of machine learning due to the appearance of problems of high dimensionality. In the literature there are numerous feature ...

    • Reduced precision discretization based on information theory 

      Ares, Brais; Morán-Fernández, Laura; Bolón-Canedo, Verónica (Elsevier, 2022-01)
      [Abstract] In recent years, new technological areas have emerged and proliferated, such as the Internet of Things or embedded systems in drones, which are usually characterized by making use of devices with strict requirements ...