A comparison of random forest based algorithms: random credal random forest versus oblique random forest
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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.
KeywordsClassification Ensemble schemes Random forest Imprecise probabilities Credal sets
This work has been supported by the Spanish “Ministerio de Economía y Competitividad” and by “Fondo Europeo de Desarrollo Regional” (FEDER) under Project TEC2015-69496-R.
Compliance with ethical standards
Conflict of interest
Carlos J. Mantas, Javier G. Castellano, Serafín Moral-García and Joaquín Abellán declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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