Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …

Bagging and boosting are two of the most well-known ensemble learning methods due to
their theoretical performance guarantees and strong experimental results. Since bagging …

The combination of multiple classifiers, commonly referred to as a classifier ensemble, has
previously demonstrated the ability to improve classification accuracy in many application …

The idea of ensemble methodology is to build a predictive model by integrating multiple
models. It is well-known that ensemble methods can be used for improving prediction …

Bagging and boosting are well-known ensemble learning methods. They combine multiple
learned base models with the aim of improving generalization performance. To date, they …

Ensemble learning techniques have achieved state-of-the-art performance in diverse
machine learning applications by combining the predictions from two or more base models …

Bagging and boosting are well-known ensemble learning methods. They combine multiple
learned base models with the aim of improving generalization performance. To date, they …

Ensemble methods that train multiple learners and then combine them for use, with Boosting
and Bagging as representatives, are a kind of state-of-theart learning approach. It is well …

Bagging and boosting are among the most popular re-sampling ensemble methods that
generate and combine a diversity of classifiers using the same learning algorithm for the …

An ensemble consists of a set of individually trained classifiers (such as neural networks or
decision trees) whose predictions are combined when classifying novel instances. Previous …