A comparison of random forest based algorithms: random credal random forest versus oblique random forest

Abstract

Random forest (RF) is an ensemble learning method, and it is considered a reference due to its excellent performance. Several improvements in RF have been published. A kind of improvement for the RF algorithm is based on the use of multivariate decision trees with local optimization process (oblique RF). Another type of improvement is to provide additional diversity for the univariate decision trees by means of the use of imprecise probabilities (random credal random forest, RCRF). The aim of this work is to compare experimentally these improvements of the RF algorithm. It is shown that the improvement in RF with the use of additional diversity and imprecise probabilities achieves better results than the use of RF with multivariate decision trees.