Now showing items 1-20 of 43

    • A comparison of performance of K-complex classification methods using feature selection 

      Hernández-Pereira, Elena; Bolón-Canedo, Verónica; Sánchez-Maroño, Noelia; Alvarez-Estevez, Diego; Moret-Bonillo, Vicente; Alonso-Betanzos, Amparo (2016-01-20)
      [Abstract] The main objective of this work is to obtain a method that achieves the best accuracy results with a low false positive rate in the classification of K-complexes, a kind of transient waveform found in the ...
    • A delayed elastic-net approach for performing adversarial attacks 

      Cancela, Brais; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo (IEEE, 2021)
      [Abstract]: With the rise of the so-called Adversarial Attacks, there is an increased concern on model security. In this paper we present two different contributions: novel measures of robustness (based on adversarial ...
    • A novel framework for generic Spark workload characterization and similar pattern recognition using machine learning 

      Garralda-Barrio, Mariano; Eiras-Franco, Carlos; Bolón-Canedo, Verónica (Elsevier, 2024-07)
      [Abstract]: Comprehensive workload characterization plays a pivotal role in comprehending Spark applications, as it enables the analysis of diverse aspects and behaviors. This understanding is indispensable for devising ...
    • A Pseudo-Label Guided Hybrid Approach for Unsupervised Domain Adaptation 

      Blanco Mallo, Eva; Bolón-Canedo, Verónica; Remeseiro, Beatriz (Springer, 2023-11-15)
      [Abstract]: Unsupervised domain adaptation focuses on reusing a model trained on a source domain in an unlabeled target domain. Two main approaches stand out in the literature: adversarial training for generating invariant ...
    • A review of green artificial intelligence: Towards a more sustainable future 

      Bolón-Canedo, Verónica; Morán-Fernández, Laura; Cancela, Brais; Alonso-Betanzos, Amparo (Elsevier B.V., 2024-09-28)
      [Abstract]: Green artificial intelligence (AI) is more environmentally friendly and inclusive than conventional AI, as it not only produces accurate results without increasing the computational cost but also ensures that ...
    • A scalable saliency-based feature selection method with instance-level information 

      Cancela, Brais; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo; Gama, João (Elsevier, 2019-11)
      [Abstract]: Classic feature selection techniques remove irrelevant or redundant features to achieve a subset of relevant features in compact models that are easier to interpret and so improve knowledge extraction. Most ...
    • Adaptive incremental transfer learning for efficient performance modeling of big data workloads 

      Garralda-Barrio, Mariano; Eiras-Franco, Carlos; Bolón-Canedo, Verónica (Elsevier, 2025-05)
      [Abstract]: The rise of data-intensive scalable computing systems, such as Apache Spark, has transformed data processing by enabling the efficient manipulation of large datasets across machine clusters. However, system ...
    • Anomaly Detection on Natural Language Processing to Improve Predictions on Tourist Preferences 

      Meira, Jorge; Carneiro, João; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo; Novais, Paulo; Marreiros, Goreti (MDPI, 2022)
      [Abstract] Argumentation-based dialogue models have shown to be appropriate for decision contexts in which it is intended to overcome the lack of interaction between decision-makers, either because they are dispersed, they ...
    • Breaking boundaries: Low-precision conditional mutual information for efficient feature selection 

      Morán-Fernández, Laura; Blanco Mallo, Eva; Sechidis, Konstantinos; Bolón-Canedo, Verónica (Elsevier Ltd, 2025)
      [Abstract]: As internet-of-things (IoT) devices proliferate, the need for efficient data processing at the network edge becomes increasingly critical due to the vast amounts of data generated. This paper presents a ...
    • Can data placement be effective for Neural Networks classification tasks? Introducing the Orthogonal Loss 

      Cancela, Brais; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo (IEEE, 2021)
      [Abstract]: Traditionally, a Neural Network classification training loss function follows the same principle: minimizing the distance between samples that belong to the same class, while maximizing the distance to the other ...
    • Case Study of Anomaly Detection and Quality Control of Energy Efficiency and Hygrothermal Comfort in Buildings 

      Eiras-Franco, Carlos; Flores, Miguel; Bolón-Canedo, Verónica; Zaragoza, Sonia; Fernández-Casal, Rubén; Naya, Salvador; Tarrío-Saavedra, Javier (2019)
      [Abstract] The aim of this work is to propose different statistical and machine learning methodologies for identifying anomalies and control the quality of energy efficiency and hygrothermal comfort in buildings. ...
    • CUDA acceleration of MI-based feature selection methods 

      Beceiro, Bieito; González-Domínguez, Jorge; Morán-Fernández, Laura; Bolón-Canedo, Verónica; Touriño, Juan (Elsevier, 2024-08)
      [Abstract]: Feature selection algorithms are necessary nowadays for machine learning as they are capable of removing irrelevant and redundant information to reduce the dimensionality of the data and improve the quality of ...
    • CUDA-JMI: Acceleration of feature selection on heterogeneous systems 

      González-Domínguez, Jorge; Expósito, Roberto R.; Bolón-Canedo, Verónica (Elsevier, 2020-01)
      [Abstract]: Feature selection is a crucial step nowadays in machine learning and data analytics to remove irrelevant and redundant characteristics and thus to provide fast and reliable analyses. Many research works have ...
    • Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry 

      Fernandes, Marta; Canito, Alda; Bolón-Canedo, Verónica; Conceição, Luís; Praça, Isabel; Marreiros, Goreti (Elsevier Ltd, 2019-06)
      [Abstract]: Proactive Maintenance practices are becoming more standard in industrial environments, with a direct and profound impact on the competitivity within the sector. These practices demand the continuous monitorization ...
    • 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 ...
    • Do all roads lead to Rome? Studying distance measures in the context of machine learning 

      Blanco Mallo, Eva; Morán-Fernández, Laura; Remeseiro, Beatriz; Bolón-Canedo, Verónica (Elsevier Ltd, 2023-09)
      [Abstract]: Many machine learning and data mining tasks are based on distance measures, so a large amount of literature addresses this aspect somehow. Due to the broad scope of the topic, this paper aims to provide an ...
    • E2E-FS: An End-to-End Feature Selection Method for Neural Networks 

      Cancela, Brais; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo (IEEE, 2023-07)
      [Abstract]: Classic embedded feature selection algorithms are often divided in two large groups: tree-based algorithms and LASSO variants. Both approaches are focused in different aspects: while the tree-based algorithms ...
    • 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 ...