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

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 …

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

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

Ensemble learning is a system that improves the performance and robustness of the
classification problems. How to combine the outputs of base classifiers is one of the …

In this paper, a new method is proposed for creating an optimized ensemble classifier. The
proposed method mitigates the issue of class imbalances by partitioning the input data into …

Ensemble learning schemes such as AdaBoost and Bagging enhance the performance of a
single classifier by combining predictions from multiple classifiers of the same type. The …

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

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 …