Listar GI-LIDIA - Artigos por autor "Guijarro-Berdiñas, Bertha"

Mostrando ítems 1-12 de 12

    • A One-Class Classification method based on Expanded Non-Convex Hulls 

      Novoa-Paradela, David; Fontenla-Romero, Óscar; Guijarro-Berdiñas, Bertha (Elsevier, 2023)
      [Abstract]: This paper presents an intuitive, robust and efficient One-Class Classification algorithm. The method developed is called OCENCH (One-class Classification via Expanded Non-Convex Hulls) and bases its operation ...

    • A scalable decision-tree-based method to explain interactions in dyadic data 

      Eiras-Franco, Carlos; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo; Bahamonde, Antonio (Elsevier, 2019-12)
      [Abstract]: Gaining relevant insight from a dyadic dataset, which describes interactions between two entities, is an open problem that has sparked the interest of researchers and industry data scientists alike. However, ...

    • Explained anomaly detection in text reviews: Can subjective scenarios be correctly evaluated? 

      Novoa-Paradela, David; Fontenla-Romero, Óscar; Guijarro-Berdiñas, Bertha (2024-07)
      In the current landscape, user opinions exert an unprecedented influence on the trajectory of companies. In the field of online review platforms, these opinions, transmitted through text reviews and numerical ratings, ...

    • Fast deep autoencoder for federated learning 

      Novoa-Paradela, David; Fontenla-Romero, Óscar; Guijarro-Berdiñas, Bertha (Elsevier Ltd, 2023-11)
      [Abstract]: This paper presents a novel, fast and privacy preserving implementation of deep autoencoders. DAEF (Deep AutoEncoder for Federated learning), unlike traditional neural networks, trains a deep autoencoder network ...

    • Fast Distributed kNN Graph Construction Using Auto-tuned Locality-sensitive Hashing 

      Eiras-Franco, Carlos; Martínez Rego, David; Kanthan, Leslie; Piñeiro, César; Bahamonde, Antonio; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo (Association for Computing Machinery, 2020)
      [Abstract]: The k-nearest-neighbors (kNN) graph is a popular and powerful data structure that is used in various areas of Data Science, but the high computational cost of obtaining it hinders its use on large datasets. ...

    • FedHEONN: Federated and homomorphically encrypted learning method for one-layer neural networks 

      Fontenla-Romero, Óscar; Guijarro-Berdiñas, Bertha; Hernández-Pereira, Elena; Pérez-Sánchez, Beatriz (Elsevier B.V., 2023)
      [Abstract]: Federated learning (FL) is a distributed approach to developing collaborative learning models from decentralized data. This is relevant to many real applications, such as in the field of the Internet of Things, ...

    • Large scale anomaly detection in mixed numerical and categorical input spaces 

      Eiras-Franco, Carlos; Martínez Rego, David; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo; Bahamonde, Antonio (Elsevier, 2019)
      [Abstract]: This work presents the ADMNC method, designed to tackle anomaly detection for large-scale problems with a mixture of categorical and numerical input variables. A flexible parametric probability measure is ...

    • 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 ...

    • 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 ...

    • Scalable Feature Selection Using ReliefF Aided by Locality-Sensitive Hashing 

      Eiras-Franco, Carlos; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo; Bahamonde, Antonio (Wiley, 2021)
      [Abstract] Feature selection algorithms, such as ReliefF, are very important for processing high-dimensionality data sets. However, widespread use of popular and effective such algorithms is limited by their computational ...

    • Simulating the Role of Norms in Processes of Social Innovation: Three Case Studies 

      Jager, Wander; Guijarro-Berdiñas, Bertha; Bouman, Loes; Antosz, Patrycja; Alonso-Betanzos, Amparo; Salt, Douglas; Polhill, J. Gary; Rodríguez Arias, Alejandro; Sánchez-Maroño, Noelia (SimSoc Consortium, 2024-01)
      [Absctract]: Norms and values are critical drivers in social innovation processes, such as community projects on sustainable energy. Simulating such processes could help uncover conditions that support these social ...

    • Sustainable transparency on recommender systems: Bayesian ranking of images for explainability 

      Paz Ruza, Jorge; Alonso-Betanzos, Amparo; Guijarro-Berdiñas, Bertha; Cancela, Brais; Eiras-Franco, Carlos (Elsevier B.V., 2024-11)
      [Abstract]: Recommender Systems have become crucial in the modern world, commonly guiding users towards relevant content or products, and having a large influence over the decisions of users and citizens. However, ensuring ...