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

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 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 …

In this paper, we perform an empirical comparison of the classification error of several
ensemble methods based on classification trees. This comparison is performed by using 14 …

Researchers from various disciplines such as pattern recognition, statistics, and machine
learning have explored the use of ensemble methodology since the late seventies. Thus …

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 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 …

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 …