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 …

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 …

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

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 …

Ensemble methods use multiple models to get better performance. Ensemble methods have
been used in multiple research fields such as computational intelligence, statistics and …

Ensemble classifier refers to a group of individual classifiers that are cooperatively trained
on data set in a supervised classification problem. In this paper we present a review of …

The ensemble paradigm for machine learning has been studied for more than two decades
and many methods, techniques and algorithms have been developed, and increasingly …

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 …

Ensemble learning is an imperative study in the domain of machine learning. Over the
previous years, ensemble learning has drawn considerable attention in the field of artificial …

Neural network ensembles (sometimes referred to as committees or classifier ensembles)
are effective techniques to improve the generalization of a neural network system …