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Distributed correlation-based feature selection in spark
dc.contributor.author | Palma Mendoza, Raúl José | |
dc.contributor.author | Marcos, Luis de | |
dc.contributor.author | Rodríguez, Daniel | |
dc.contributor.author | Alonso-Betanzos, Amparo | |
dc.date.accessioned | 2023-12-04T14:29:07Z | |
dc.date.available | 2023-12-04T14:29:07Z | |
dc.date.issued | 2019-09 | |
dc.identifier.citation | 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, doi: 10.1016/j.ins.2018.10.052. | es_ES |
dc.identifier.issn | 0020-0255 | |
dc.identifier.uri | http://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.sponsorship | The 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.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/ES/ALGORITMOS ESCALABLES DE APRENDIZAJE COMPUTACIONAL: MAS ALLA DE LA CLASIFICACION Y LA REGRESION | es_ES |
dc.relation | info: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 GAMIFICACION | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.ins.2018.10.052 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional | es_ES |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Feature selection | es_ES |
dc.subject | Scalability | es_ES |
dc.subject | Big data | es_ES |
dc.subject | Apache spark | es_ES |
dc.subject | CFS | es_ES |
dc.subject | Correlation | es_ES |
dc.title | Distributed correlation-based feature selection in spark | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Information Sciences | es_ES |
UDC.issue | 496 | es_ES |
UDC.startPage | 287 | es_ES |
UDC.endPage | 299 | es_ES |
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