2026-04-17T23:16:46Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/62072022-11-21T12:44:04Zcom_10259_5377com_10259_5086com_10259_2604col_10259_5378
Juez Gil, Mario
747
500
Arnaiz González, Álvar
39
600
0000-0001-6965-0237
Rodríguez Diez, Juan José
477
600
López Nozal, Carlos
322
600
0000-0001-8462-212X
García Osorio, César
212
600
0000-0002-1206-1084
2021-11-23T08:27:57Z
2021-11-23T08:27:57Z
2021-10
1566-2535
http://hdl.handle.net/10259/6207
10.1016/j.inffus.2021.03.007
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.
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.
application/pdf
eng
Elsevier
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/ES/ALGORITMOS DE ENSEMBLES PARA PROBLEMAS DE SALIDAS MULTIPLES. NUEVOS DESARROLLOS Y APLICACIONES
info:eu-repo/grantAgreement/Junta de Castilla y León//BU085P17//Minería de datos para le mejora del mantenimiento y disponibilidad de máquinas de altas presiones
info:eu-repo/grantAgreement/Junta de Castilla y León//BU055P20//Métodos y Aplicaciones Industriales del Aprendizaje Semisupervisado
info:eu-repo/grantAgreement/Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona//LCF%2FPR%2FPR18%2F51130007
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
Rotation Forest
Random Forest
Ensemble learning
Machine learning
Big Data
Spark
Informática
Computer science
Rotation Forest for Big Data
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
THUMBNAIL
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oai:riubu.ubu.es:10259/6207
2022-11-21 13:44:04.439
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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