| dc.contributor.author | Bolón-Canedo, Verónica | |
| dc.contributor.author | Alonso-Betanzos, Amparo | |
| dc.date.accessioned | 2024-02-01T18:44:55Z | |
| dc.date.available | 2024-02-01T18:44:55Z | |
| dc.date.issued | 2019 | |
| dc.identifier.citation | Bolón-Canedo, V. and Alonso-Betanzos, A. (2019) ‘Ensembles for Feature Selection: A Review and Future Trends’, Information Fusion, 52, pp. 1–12. doi:10.1016/j.inffus.2018.11.008. | es_ES |
| dc.identifier.issn | 1566-2535 | |
| dc.identifier.issn | 1872-6305 | |
| dc.identifier.uri | http://hdl.handle.net/2183/35335 | |
| dc.description | © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Bolón-Canedo, V. and Alonso-Betanzos, A. (2019) ‘Ensembles for Feature Selection: A Review and Future Trends’ has been accepted for publication in: Information Fusion, 52, pp. 1–12. The Version of Record is available online at https://doi.org/10.1016/j.inffus.2018.11.008. | es_ES |
| dc.description.abstract | [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 results. Normally, it has been commonly employed for classification, but it can be used to improve other disciplines such as feature selection. Feature selection consists of selecting the relevant features for a problem and discard those irrelevant or redundant, with the main goal of improving classification accuracy. In this work, we provide the reader with the basic concepts necessary to build an ensemble for feature selection, as well as reviewing the up-to-date advances and commenting on the future trends that are still to be faced. | es_ES |
| dc.description.sponsorship | This research has been financially supported in part by the Spanish Ministerio de Economa y Competitividad (research project TIN 2015-65069-C2-1-R), by the Xunta de Galicia (research projects GRC2014/035 and the Centro Singular de Investigación de Galicia, accreditation 2016–2019, Ref. ED431G/01) and by the European Union (FEDER/ERDF). | es_ES |
| dc.description.sponsorship | Xunta de Galicia; GRC2014/035 | es_ES |
| dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
| dc.language.iso | eng | es_ES |
| dc.publisher | Elsevier | es_ES |
| dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2015-65069-C2-1-R/ALGORITMOS ESCALABLES DE APRENDIZAJE COMPUTACIONAL: MAS ALLA DE LA CLASIFICACION Y LA REGRESION | es_ES |
| dc.relation.uri | https://doi.org/10.1016/j.inffus.2018.11.008 | es_ES |
| dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional | es_ES |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
| dc.subject | Ensemble learning | es_ES |
| dc.subject | Feature selection | es_ES |
| dc.title | Ensembles for feature selection: A review and future trends | es_ES |
| dc.type | info:eu-repo/semantics/article | es_ES |
| dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
| UDC.journalTitle | Information Fusion | es_ES |
| UDC.volume | 52 | es_ES |
| UDC.startPage | 1 | es_ES |
| UDC.endPage | 12 | es_ES |
| dc.identifier.doi | 10.1016/j.inffus.2018.11.008 | |
| UDC.coleccion | Investigación | es_ES |
| UDC.departamento | Ciencias da Computación e Tecnoloxías da Información | es_ES |
| UDC.grupoInv | Laboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA) | es_ES |
