Rotation Forest for Big Data

The Rotation Forest classifier is a successful ensemble method for a wide variety of data mining applications. However, the way in which Rotation Forest transforms the feature space through PCA, although powerful, penalizes training and prediction times, making it unfeasible for Big Data. In this pa...

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Autores: Juez Gil, Mario, Arnaiz González, Álvar, Rodríguez Diez, Juan José, López Nozal, Carlos, García Osorio, César
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Burgos (UBU)
Repositorio:Repositorio Institucional de la Universidad de Burgos (RIUBU)
OAI Identifier:oai:riubu.ubu.es:10259/6207
Acceso en línea:http://hdl.handle.net/10259/6207
Access Level:acceso abierto
Palabra clave:Rotation Forest
Random Forest
Ensemble learning
Machine learning
Big Data
Spark
Informática
Computer science
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spelling Rotation Forest for Big DataJuez Gil, MarioArnaiz González, ÁlvarRodríguez Diez, Juan JoséLópez Nozal, CarlosGarcía Osorio, CésarRotation ForestRandom ForestEnsemble learningMachine learningBig DataSparkInformáticaComputer scienceThe Rotation Forest classifier is a successful ensemble method for a wide variety of data mining applications. However, the way in which Rotation Forest transforms the feature space through PCA, although powerful, penalizes training and prediction times, making it unfeasible for Big Data. In this paper, a MapReduce Rotation Forest and its implementation under the Spark framework are presented. The proposed MapReduce Rotation Forest behaves in the same way as the standard Rotation Forest, training the base classifiers on a rotated space, but using a functional implementation of the rotation that enables its execution in Big Data frameworks. Experimental results are obtained using different cloud-based cluster configurations. Bayesian tests are used to validate the method against two ensembles for Big Data: Random Forest and PCARDE classifiers. Our proposal incorporates the parallelization of both the PCA calculation and the tree training, providing a scalable solution that retains the performance of the original Rotation Forest and achieves a competitive execution time (in average, at training, more than 3 times faster than other PCA-based alternatives). In addition, extensive experimentation shows that by setting some parameters of the classifier (i.e., bootstrap sample size, number of trees, and number of rotations), the execution time is reduced with no significant loss of performance using a small ensemble.This work was supported through project TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía y Competitividad of the Spanish Government, projects BU085P17 and BU055P20 (JCyL/FEDER, UE) of the Junta de Castilla y León, Spain (both projects co-financed through European Union FEDER funds), and by the Consejería de Educación of the Junta de Castilla y León, Spain and the European Social Fund through a pre-doctoral grant (EDU/1100/2017). The project leading to these results has received also funding from “la Caixa” Foundation, Spain , under agreement LCF/PR/PR18/51130007. This material is based upon work supported by Google Cloud.Elsevier202120212021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10259/6207reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)instname:Universidad de Burgos (UBU)InglésInformation Fusion. 2021, V. 74, p. 39-49https://doi.org/10.1016/j.inffus.2021.03.007info:eu-repo/grantAgreement/MINECO//TIN2015-67534-Pinfo:eu-repo/grantAgreement/Junta de Castilla y León//BU085P17info:eu-repo/grantAgreement/Junta de Castilla y León//BU055P20info:eu-repo/grantAgreement/Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona//LCF%2FPR%2FPR18%2F51130007Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riubu.ubu.es:10259/62072026-05-28T07:56:11Z
dc.title.none.fl_str_mv Rotation Forest for Big Data
title Rotation Forest for Big Data
spellingShingle Rotation Forest for Big Data
Juez Gil, Mario
Rotation Forest
Random Forest
Ensemble learning
Machine learning
Big Data
Spark
Informática
Computer science
title_short Rotation Forest for Big Data
title_full Rotation Forest for Big Data
title_fullStr Rotation Forest for Big Data
title_full_unstemmed Rotation Forest for Big Data
title_sort Rotation Forest for Big Data
dc.creator.none.fl_str_mv Juez Gil, Mario
Arnaiz González, Álvar
Rodríguez Diez, Juan José
López Nozal, Carlos
García Osorio, César
author Juez Gil, Mario
author_facet Juez Gil, Mario
Arnaiz González, Álvar
Rodríguez Diez, Juan José
López Nozal, Carlos
García Osorio, César
author_role author
author2 Arnaiz González, Álvar
Rodríguez Diez, Juan José
López Nozal, Carlos
García Osorio, César
author2_role author
author
author
author
dc.subject.none.fl_str_mv Rotation Forest
Random Forest
Ensemble learning
Machine learning
Big Data
Spark
Informática
Computer science
topic Rotation Forest
Random Forest
Ensemble learning
Machine learning
Big Data
Spark
Informática
Computer science
description The Rotation Forest classifier is a successful ensemble method for a wide variety of data mining applications. However, the way in which Rotation Forest transforms the feature space through PCA, although powerful, penalizes training and prediction times, making it unfeasible for Big Data. In this paper, a MapReduce Rotation Forest and its implementation under the Spark framework are presented. The proposed MapReduce Rotation Forest behaves in the same way as the standard Rotation Forest, training the base classifiers on a rotated space, but using a functional implementation of the rotation that enables its execution in Big Data frameworks. Experimental results are obtained using different cloud-based cluster configurations. Bayesian tests are used to validate the method against two ensembles for Big Data: Random Forest and PCARDE classifiers. Our proposal incorporates the parallelization of both the PCA calculation and the tree training, providing a scalable solution that retains the performance of the original Rotation Forest and achieves a competitive execution time (in average, at training, more than 3 times faster than other PCA-based alternatives). In addition, extensive experimentation shows that by setting some parameters of the classifier (i.e., bootstrap sample size, number of trees, and number of rotations), the execution time is reduced with no significant loss of performance using a small ensemble.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10259/6207
url http://hdl.handle.net/10259/6207
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Information Fusion. 2021, V. 74, p. 39-49
https://doi.org/10.1016/j.inffus.2021.03.007
info:eu-repo/grantAgreement/MINECO//TIN2015-67534-P
info:eu-repo/grantAgreement/Junta de Castilla y León//BU085P17
info:eu-repo/grantAgreement/Junta de Castilla y León//BU055P20
info:eu-repo/grantAgreement/Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona//LCF%2FPR%2FPR18%2F51130007
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)
instname:Universidad de Burgos (UBU)
instname_str Universidad de Burgos (UBU)
reponame_str Repositorio Institucional de la Universidad de Burgos (RIUBU)
collection Repositorio Institucional de la Universidad de Burgos (RIUBU)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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