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.

旋转森林分类器是适用于各种数据挖掘应用程序的成功集成方法。但是，Rotation Forest通过PCA变换要素空间的方式虽然功能强大，但却会浪费训练和预测时间，因此不适用于大数据。本文介绍了MapReduce旋转林及其在Spark框架下的实现。拟议的MapReduce旋转林的行为与标准旋转林相同，在旋转的空间上训练基本分类器，但是使用旋转的功能实现，使其能够在大数据框架中执行。使用不同的基于云的群集配置可获得实验结果。贝叶斯测试用于针对大数据的两个集合来验证该方法：随机森林和PCARDE分类器。我们的建议结合了PCA计算和树训练的并行化，提供了可扩展的解决方案，该解决方案保留了原始Rotation Forest的性能并实现了具有竞争力的执行时间（在训练中，平均而言，其速度是其他PC​​A-的3倍以上）替代方案）。另外，广泛的实验表明，通过设置分类器的某些参数（即，引导程序样本大小，树数和旋转数），可以减少执行时间，并且通过使用较小的集成就不会显着降低性能。比其他基于PCA的替代产品快3倍以上）。另外，广泛的实验表明，通过设置分类器的某些参数（即，引导程序样本大小，树数和旋转数），可以减少执行时间，并且通过使用较小的集成就不会显着降低性能。比其他基于PCA的替代产品快3倍以上）。另外，广泛的实验表明，通过设置分类器的某些参数（即，引导程序样本大小，树数和旋转数），可以减少执行时间，并且通过使用较小的集成就不会显着降低性能。