Increasing diversity in Random Forest learning algorithm via imprecise probabilities Abellán Mulero, Joaquín Mantas Ruiz, Carlos Javier García Castellano, Francisco Javier Moral García, Serafín Classification Ensemble schemes Random forest Imprecise probabilities Uncertainty measures Random Forest (RF) learning algorithm is considered a classifier of reference due its excellent performance. Its success is based on the diversity of rules generated from decision trees that are built via a procedure that randomizes instances and features. To find additional procedures for increasing the diversity of the trees is an interesting task. It has been considered a new split criterion, based on imprecise probabilities and general uncertainty measures, that has a clear dependence of a parameter and has shown to be more successful than the classic ones. Using that criterion in RF scheme, join with a random procedure to select the value of that parameter, the diversity of the trees in the forest and the performance are increased. This fact gives rise to a new classification algorithm, called Random Credal Random Forest (RCRF). The new method represents several improvements with respect to the classic RF: the use of a more successful split criterion which is more robust to noise than the classic ones; and an increasing of the randomness which facilitates the diversity of the rules obtained. In an experimental study, it is shown that this new algorithm is a clear enhancement of RF, especially when it applied on data sets with class noise, where the standard RF has a notable deterioration. The problem of overfitting that appears when RF classifies data sets with class noise is solved with RCRF. This new algorithm can be considered as a powerful alternative to be used on data with or without class noise. 2024-02-07T10:05:50Z 2024-02-07T10:05:50Z 2018-05-01 info:eu-repo/semantics/article Published version: Abellán, J., Mantas, C. & Castellano, J. G. & Moral-García, S. (2018). Increasing diversity in Random Forest learning algorithm via imprecise probabilities. Expert Systems with Applications 97: 228–243. Doi: 10.1016/j.eswa.2017.12.029 https://hdl.handle.net/10481/88516 10.1016/j.eswa.2017.12.029 eng info:eu-repo/semantics/openAccess Elsevier