2026-04-17T23:14:57Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/62072022-11-21T12:44:04Zcom_10259_5377com_10259_5086com_10259_2604col_10259_5378

00925njm 22002777a 4500 dc Juez Gil, Mario author Arnaiz González, Álvar author Rodríguez Diez, Juan José author López Nozal, Carlos author García Osorio, César author 2021-10 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. 1566-2535 http://hdl.handle.net/10259/6207 10.1016/j.inffus.2021.03.007 Rotation Forest Random Forest Ensemble learning Machine learning Big Data Spark Rotation Forest for Big Data