Labeled training data are used for challenging medical image segmentation problems to learn different characteristics of the relevant domain. In this paper, we examine random forest (RF) classifiers, ...

We propose an improved random forest classifier that performs classification with a minimum number of trees. The proposed method iteratively removes some unimportant features. Based on the number of ...

Building on random forests (RFs) and random intersection trees (RITs) and through extensive, biologically inspired simulations, we developed the iterative random forest algorithm (iRF). iRF trains a ...

Our analysis revealed that Random Forest consistently outperformed other models in balancing predictive accuracy and alignment with financial forecasts. Among the tested configurations, the ...