2026-06-17T16:29:23Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/100322025-01-25T01:05:24Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

Rodríguez Diez, Juan José Juez Gil, Mario Arnaiz González, Álvar Kuncheva, Ludmila I. . 2025-01-24T09:21:02Z 2025-01-24T09:21:02Z 2020-08 0957-4174 http://hdl.handle.net/10259/10032 10.1016/j.eswa.2020.113376 Over the past few decades, the remarkable prediction capabilities of ensemble methods have been used within a wide range of applications. Maximization of base-model ensemble accuracy and diversity are the keys to the heightened performance of these methods. One way to achieve diversity for training the base models is to generate artificial/synthetic instances for their incorporation with the original instances. Recently, the mixup method was proposed for improving the classification power of deep neural networks (Zhang, Cissé, Dauphin, and Lopez-Paz, 2017). Mixup method generates artificial instances by combining pairs of instances and their labels, these new instances are used for training the neural networks promoting its regularization. In this paper, new regression tree ensembles trained with mixup, which we will refer to as Mixup Regression Forest, are presented and tested. The experimental study with 61 datasets showed that the mixup approach improved the results of both Random Forest and Rotation Forest. eng http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional Mixup Regression Random forest Rotation forest An experimental evaluation of mixup regression forests info:eu-repo/semantics/article