RT Journal Article
T1 RF_MSU: A multivariate symmetrical uncertainty-based random forest
A1 García Torres, Miguel
A1 Saucedo Francisco, 
A1 Divina, Federico
A1 Gómez-Guerrero, Santiago Gómez-Guerrero
K1 Random forests
K1 Machine learning
K1 Multivariate symmetrical uncertainty
AB Decision Trees (DTs) have become very popular classifiers due to their good performance and, most of all, their interpretability. In addition, the machine learning community is also paying attention to Random Forests (RFs) since they defy the interpretability-accuracy tradeoff. Most RFs strategies are based on univariate measures, a fact that may limit the capability of identifying the interaction among more than two features. In order to overcome this problem many multivariate approaches have been proposed. However, most of them are based on finding linear or non-linear combinations of features. In this work, we propose a novel univariate RF strategy that builds DTs using the Multivariate Symmetrical Uncertainty (MSU) measure as splitting criterion. The proposal, referred to as RF_MSU, was tested on high-dimensional datasets and compared to state-of-the-art univariate and multivariate DTs and RFs classifiers. Results suggest that RF_MSU is capable of finding simpler rules than other RFs approaches while keeping a high predictive power equivalent to that of multivariate approaches. The DT strategies considered obtained simpler models than RF_MSU, but at the expense of degrading the classifier. Thus, we can conclude that RF_MSU is a RF-based classifier that achieves a good trade-off between the performance and the complexity of the model.
PB Elsevier
YR 2026
FD 2026-01-01
LK https://hdl.handle.net/10433/24336
UL https://hdl.handle.net/10433/24336
LA en
NO García-Torres, M., Saucedo, F., Divina, F., & Gómez-Guerrero, S. (2025). RFMSU: A multivariate symmetrical uncertainty-based random forest. Pattern Recognition, 111939.
NO Proyectos de investigación'PID2020-117954RB-C21','PY20-00870', 'UPO-138516'
NO Departamento de Deporte e Informática
NO Escuela Politécnica Superior
DS RIO
RD May 7, 2026