The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a
higher resolution than individual classifiers. Random forest is one of the types of ensemble …

Random forests on the one hand, and neural networks on the other hand, have met great
success in the machine learning community for their predictive performance. Combinations …

In this study, we propose a lightweight surrogate random forest (L-SRF) algorithm that can
be interpreted through a new rule distillation method. The common surrogate models …

As the demand for human-friendly computing increases, research on pupil tracking to
facilitate human–machine interactions (HCIs) is being actively conducted. Several …

Deep forest is a non-differentiable deep model that has achieved impressive empirical
success across a wide variety of applications, especially on categorical/symbolic or mixed …

The deep random forest (DRF) has recently gained new attention in deep learning because
it has a high performance similar to that of a deep neural network (DNN) and does not rely …

In essence, this Ph. D. strives to fathom the crossroads of tree-based methods and modern
neural architectures, exploring potential synergies, benefits, and theoretical underpinnings …

Judges have to consider the motives of the defendant's side and the sequence of actions of
all judicial subjects in the process of sentencing. Text analysis methods based on word …

Research Article A New Random Forest Algorithm Based on Learning Automata Page 1
Research Article A New Random Forest Algorithm Based on Learning Automata Mohammad …

DISSERTATION LEVERAGING ENSEMBLES: BALANCING TIMELINESS AND ACCURACY
FOR MODEL TRAINING OVER VOLUMINOUS DATASETS Submitted by Wa Page 1 …