Ensemble methodology, which builds a classification model by integrating multiple
classifiers, can be used for improving prediction performance. Researchers from various …

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 …

Ensemble methods are considered the state‐of‐the art solution for many machine learning
challenges. Such methods improve the predictive performance of a single model by training …

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 …

Boosting is used to determine the order in which classifiers are aggregated in a bagging
ensemble. Early stopping in the aggregation of the classifiers in the ordered bagging …

The order in which classifiers are aggregated in ensemble methods can be an important tool
in the identification of subsets of classifiers that, when combined, perform better than the …

The idea of ensemble learning is to build a prediction model by combining the strengths of a
collection of simpler base models. We have already seen a number of examples that fall into …

In this paper we propose a novel approach for ensemble construction based on the use of
nonlinear projections to achieve both accuracy and diversity of individual classifiers. The …

Over the last two decades, the machine learning and related communities have conducted
numerous studies to improve the performance of a single classifier by combining several …

Ensemble learning has gained success in machine learning with major advantages over
other learning methods. Bagging is a prominent ensemble learning method that creates …