RT Journal Article
T1 RFMSU: A multivariate symmetrical uncertainty-based random forest
A1 García Torres, Miguel
A1 Saucedo, Francisco
A1 Divina, Federico
A1 Gómez-Guerrero, Santiago
K1 Decision tree
K1 Random forest
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 RFMSU, was tested on high-dimensional datasets and compared to state-of-the-art univariate and multivariate DTs and RFs classifiers. Results suggest that RFMSU 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 RFMSU, but at the expense of degrading the classifier. Thus, we can conclude that RFMSU 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/10433/24256
UL https://hdl.handle.net/10433/24256
LA en
NO Pattern Recognition, vol. 169, p. 111939
NO Proyectos de investigaciónPID2020-117954RB-C21PY20-00870UPO-138516
NO Data Science and Big Data Lab
NO Universidad Pablo de Olavide
DS RIO
RD May 9, 2026