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 …

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 is one of the well-known ensemble learning methods, which combines several
classifiers trained on different subsamples of the dataset. However, a drawback of bagging …

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 …

In the past two decades, some successful ensemble learning algorithms have been
proposed, typically as Bagging, AdaBoost, DECORATE, etc. Although all adopting diversity …

Ensemble learning is a popular classification method where many individual simple learners
contribute to a final prediction. Constructing an ensemble of learners has been shown to …

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 …

Classification performance of an ensemble method can be deciphered by studying the bias
and variance contribution to its classification error. Statistically, the bias and variance of a …