Listar por autor "Bolón-Canedo, Verónica"
Mostrando ítems 21-35 de 35
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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, ... -
Finding a needle in a haystack: insights on feature selection for classification tasks
Morán-Fernández, Laura; Bolón-Canedo, Verónica (Springer, 2024-04)[Abstract]: The growth of Big Data has resulted in an overwhelming increase in the volume of data available, including the number of features. Feature selection, the process of selecting relevant features and discarding ... -
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 ... -
Mejora de la motivación del alumnado mediante la realización de un debate en la materia de Sistemas Inteligentes
Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo; Alonso Ríos, David; Fernández-Varela, Isaac; Varela, Daniel (Asociación de Enseñantes Universitarios de la Informática (AENUI), 2018)[Resumen]: En nuestra asignatura “Sistemas Inteligentes” del Grado en Ingeniería Informática de la Universidad da Coruña existe una brecha entre los contenidos que se imparten (más teóricos y sentando las bases de la ... -
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 Mallo, 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 ... -
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 ...
