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 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 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 …

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 methods aim at improving the predictive performance of a given statistical
learning or model fitting technique. The general principle of ensemble methods is to …

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

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 …

Predictive performance is the most important concern on many classification and regression
problems. Ensemble learning algorithms combine the predictions from multiple models and …