Despite significant successes achieved in knowledge discovery, traditional machine
learning methods may fail to obtain satisfactory performances when dealing with complex …

Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …

The number of trees T in the random forest (RF) algorithm for supervised learning has to be
set by the user. It is unclear whether T should simply be set to the largest computationally …

Diabetic retinopathy (DR) is a major cause of human vision loss worldwide. Slowing down
the progress of the disease requires early screening. However, the clinical diagnosis of DR …

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

Random Forest is an ensemble of decision trees based on the bagging and random
subspace concepts. As suggested by Breiman, the strength of unstable learners and the …

The adoption of deep neural networks for profiled side-channel attacks provides powerful
options for leakage detection and key retrieval of secure products. When training a neural …

Random forest (RF) is one of the most popular parallel ensemble methods, using decision
trees as classifiers. One of the hyper-parameters to choose from for RF fitting is the …

In this paper, we propose a new decision forest algorithm that builds a set of highly accurate
decision trees by exploiting the strength of all non-class attributes available in a data set …

The number of component classifiers chosen for an ensemble greatly impacts the prediction
ability. In this paper, we use a geometric framework for a priori determining the ensemble …