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Multithreaded and Spark parallelization of feature selection filters
dc.contributor.author | Eiras-Franco, Carlos | |
dc.contributor.author | Bolón-Canedo, Verónica | |
dc.contributor.author | Ramos Garea, Sabela | |
dc.contributor.author | González-Domínguez, Jorge | |
dc.contributor.author | Alonso-Betanzos, Amparo | |
dc.contributor.author | Touriño, Juan | |
dc.date.accessioned | 2023-12-21T11:33:01Z | |
dc.date.available | 2023-12-21T11:33:01Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | C. Eiras-Franco, V. Bolón-Canedo, S. Ramos, J. González-Domínguez, A. Alonso-Betanzos, and J. Touriño, "Multithreaded and Spark parallelization of feature selection filters", Journal of Computational Science, Vol. 17, Part 3, Nov. 2016, Pp. 609-619, https://doi.org/10.1016/j.jocs.2016.07.002 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/34589 | |
dc.description | ©2016 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/bync-nd/4.0/. This version of the article has been accepted for publication in Journal of Computational Science. The Version of Record is available online at https://doi.org/10.1016/j.jocs.2016.07.002 | es_ES |
dc.description | Versión final aceptada de: C. Eiras-Franco, V. Bolón-Canedo, S. Ramos, J. González-Domínguez, A. Alonso-Betanzos, and J. Touriño, "Multithreaded and Spark parallelization of feature selection filters", Journal of Computational Science, Vol. 17, Part 3, Nov. 2016, Pp. 609-619 | es_ES |
dc.description.abstract | [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 this functionality, Weka is one of the most popular ones, although the implementations it provides struggle when processing large datasets, requiring excessive times to be practical. Parallel processing can help alleviate this problem, effectively allowing users to work with Big Data. The computational power of multicore machines can be harnessed by using multithreading and distributed programming, effectively helping to tackle larger problems. Both these techniques can dramatically speed up the feature selection process allowing users to work with larger datasets. The reimplementation of four popular feature selection algorithms included in Weka is the focus of this work. Multithreaded implementations previously not included in Weka as well as parallel Spark implementations were developed for each algorithm. Experimental results obtained from tests on real-world datasets show that the new versions offer significant reductions in processing times. | es_ES |
dc.description.sponsorship | This work has been financed in part by Xunta de Galicia under Research Network R2014/041 and project GRC2014/035, and by Spanish Ministerio de Economía y Competitividad under projects TIN2012-37954 and TIN-2015-65069-C2-1-R, partially funded by FEDER funds of the European Union. V. Bolón-Canedo acknowledges support of the Xunta de Galicia under postdoctoral Grant code ED481B 2014/164-0. Additionally, the collaboration of Jorge Veiga on setting up and using the MREv tool for Spark execution was essential for this work. | es_ES |
dc.description.sponsorship | Xunta de Galicia; R2014/041 | es_ES |
dc.description.sponsorship | Xunta de Galicia; GRC2014/035 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481B 2014/164-0 | es_ES |
dc.language.iso | eng | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2012-37954/ES/ALGORITMOS DE APRENDIZAJE COMPUTACIONAL EN ENTORNOS DISTRIBUIDOS | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN-2015-65069-C2-1-R/ALGORITMOS ESCALABLES DE APRENDIZAJE COMPUTACIONAL: MAS ALLA DE LA CLASIFICACION Y LA REGRESION | es_ES |
dc.relation.isversionof | https://doi.org/10.1016/j.jocs.2016.07.002 | |
dc.relation.uri | https://doi.org/10.1016/j.jocs.2016.07.002 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Multithreading | es_ES |
dc.subject | Spark | es_ES |
dc.subject | Feature selection | es_ES |
dc.subject | Machine learning | es_ES |
dc.title | Multithreaded and Spark parallelization of feature selection filters | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Journal of Computational Science | es_ES |
UDC.volume | 17 | es_ES |
UDC.issue | 3 | es_ES |
UDC.startPage | 609 | es_ES |
UDC.endPage | 619 | es_ES |
dc.identifier.doi | 10.1016/j.jocs.2016.07.002 |
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