RT info:eu-repo/semantics/article
T1 An experimental evaluation of mixup regression forests
A1 Rodríguez Diez, Juan José
A1 Juez Gil, Mario
A1 Arnaiz González, Álvar
A1 Kuncheva, Ludmila I. .
K1 Mixup
K1 Regression
K1 Random forest
K1 Rotation forest
K1 Redes neuronales artificiales
K1 Neural networks (Computer science)
AB 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.
PB Elsevier
SN 0957-4174
YR 2020
FD 2020-08
LK http://hdl.handle.net/10259/10032
UL http://hdl.handle.net/10259/10032
LA eng
NO This work was supported through project TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía y Competitividad of the Spanish Government, project BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León (both projects co-financed through European Union FEDER funds), and by the Consejería de Educación of the Junta de Castilla y León and the European Social Fund through a pre-doctoral grant (EDU/1100/2017). The third author is grateful for a Mobility Grant (CAS19/00100) from the Ministerio de Ciencia, Innovación y Universidades of the Spanish Government. The authors gratefully acknowledge the support of NVIDIA Corporation and its donation of the TITAN Xp GPUs that facilitated this research.
DS Repositorio Institucional de la Universidad de Burgos
RD 03-mar-2025