Listar por autor "Bolón-Canedo, Verónica"

Mostrando ítems 1-20 de 26

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

      Hernández-Pereira, Elena; Bolón-Canedo, Verónica; Sánchez-Maroño, Noelia; Álvarez-Estévez, 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 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 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 ...

    • 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 ...

    • 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-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 ...

    • 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 ...

    • Fast anomaly detection with locality-sensitive hashing and hyperparameter autotuning 

      Meira, Jorge; Eiras-Franco, Carlos; Bolón-Canedo, Verónica; Marreiros, Goreti; Alonso-Betanzos, Amparo (Elsevier, 2022-08)
      [Abstract]: This paper presents LSHAD, an anomaly detection (AD) method based on Locality Sensitive Hashing (LSH), capable of dealing with large-scale datasets. The resulting algorithm is highly parallelizable and its ...

    • Feature Selection in Big Image Datasets 

      Figueira-Domínguez, J. Guzmán; Bolón-Canedo, Verónica; Remeseiro, Beatriz (MDPI AG, 2020-08-24)
      [Abstract] In computer vision, current feature extraction techniques generate high dimensional data. Both convolutional neural networks and traditional approaches like keypoint detectors are used as extractors of high-level ...

    • Feature Selection With Limited Bit Depth Mutual Information for Embedded Systems 

      Morán-Fernández, Laura; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo (MDPI AG, 2018-09-17)
      [Abstract] Data is growing at an unprecedented pace. With the variety, speed and volume of data flowing through networks and databases, newer approaches based on machine learning are required. But what is really big in Big ...

    • Fed-mRMR: A lossless federated feature selection method 

      Hermo González, Jorge; Bolón-Canedo, Verónica; Ladra, Susana (Elsevier, 2024-05)
      [Abstract]: Feature selection has become a mandatory task in data mining, due to the overwhelming amount of features in Big Data problems. To handle this high-dimensional data and avoid the well-known curse of dimensionality, ...

    • How Important Is Data Quality? Best Classifiers vs Best Features 

      Morán-Fernández, Laura; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo (Elsevier, 2021)
      [Abstract] The task of choosing the appropriate classifier for a given scenario is not an easy-to-solve question. First, there is an increasingly high number of algorithms available belonging to different families. And ...

    • Insights into distributed feature ranking 

      Bolón-Canedo, Verónica; Sechidis, Konstantinos; Sánchez-Maroño, Noelia; Alonso-Betanzos, Amparo; Brown, Gavin (Elsevier, 2019)
      [Abstract]: In an era in which the volume and complexity of datasets is continuously growing, feature selection techniques have become indispensable to extract useful information from huge amounts of data. However, existing ...

    • Low-Precision Feature Selection on Microarray Data: An Information Theoretic Approach 

      Morán-Fernández, Laura; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo (Springer, 2022)
      [Abstract] The number of interconnected devices, such as personal wearables, cars, and smart-homes, surrounding us every day has recently increased. The Internet of Things devices monitor many processes, and have the ...

    • 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 ...