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dc.contributor.authorPalma Mendoza, Raúl José
dc.contributor.authorMarcos, Luis de
dc.contributor.authorRodríguez, Daniel
dc.contributor.authorAlonso-Betanzos, Amparo
dc.date.accessioned2023-12-04T14:29:07Z
dc.date.available2023-12-04T14:29:07Z
dc.date.issued2019-09
dc.identifier.citationR.-J. Palma-Mendoza, L. de-Marcos, D. Rodriguez, y A. Alonso-Betanzos, «Distributed correlation-based feature selection in spark», Information Sciences, vol. 496, pp. 287-299, sep. 2019, doi: 10.1016/j.ins.2018.10.052.es_ES
dc.identifier.issn0020-0255
dc.identifier.urihttp://hdl.handle.net/2183/34420
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 "R.-J. Palma-Mendoza, L. de-Marcos, D. Rodriguez, y A. Alonso-Betanzos, «Distributed correlation-based feature selection in spark», Information Sciences, vol. 496, pp. 287-299, sep. 2019" has been accepted for publication in Information Sciences. The Version of Record is available online at doi: 10.1016/j.ins.2018.10.052.es_ES
dc.description.abstract[Abstract]: Feature selection (FS) is a key preprocessing step in data mining. CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed version of the CFS algorithm, capable of dealing with the large volumes of data typical of big data applications. Two versions of the algorithm were implemented and compared using the Apache Spark cluster computing model, currently gaining popularity due to its much faster processing times than Hadoop’s MapReduce model. We tested our algorithms on four publicly available datasets, each consisting of a large number of instances and two also consisting of a large number of features. The results show that our algorithms were superior in terms of both time-efficiency and scalability. In leveraging a computer cluster, they were able to handle larger datasets than the non-distributed WEKA version while maintaining the quality of the results, i.e., exactly the same features were returned by our algorithms when compared to the original algorithm available in WEKA.es_ES
dc.description.sponsorshipThe authors thank CESGA for use of their supercomputing resources. This research has been partially supported by the Spanish Ministerio de Economía y Competitividad (research projects TIN 2015-65069-C2-1R, TIN2016-76956-C3-3-R), the Xunta de Galicia (Grants GRC2014/035 and ED431G/01) and the European Union Regional Development Funds. R. Palma-Mendoza holds a scholarship from the Spanish Fundación Carolina and the National Autonomous University of Honduras.es_ES
dc.description.sponsorshipXunta de Galicia; GRC2014/035es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2015-65069-C2-1-R/ES/ALGORITMOS ESCALABLES DE APRENDIZAJE COMPUTACIONAL: MAS ALLA DE LA CLASIFICACION Y LA REGRESIONes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-76956-C3-3-R/ES/INNOVACION EN LA MEJORA DE LA CALIDAD DE LOS PROCESOS IMPULSADOS POR LAS PERSONAS A TRAVES DE SIMULACION Y GAMIFICACIONes_ES
dc.relation.urihttps://doi.org/10.1016/j.ins.2018.10.052es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacionales_ES
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectFeature selectiones_ES
dc.subjectScalabilityes_ES
dc.subjectBig dataes_ES
dc.subjectApache sparkes_ES
dc.subjectCFSes_ES
dc.subjectCorrelationes_ES
dc.titleDistributed correlation-based feature selection in sparkes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleInformation Scienceses_ES
UDC.issue496es_ES
UDC.startPage287es_ES
UDC.endPage299es_ES


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