During the last decades, in the area of machine learning and data mining, the development
of ensemble methods has gained a significant attention from the scientific community …

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 …

Despite significant successes achieved in knowledge discovery, traditional machine
learning methods may fail to obtain satisfactory performances when dealing with complex …

Ensemble learning techniques have achieved state-of-the-art performance in diverse
machine learning applications by combining the predictions from two or more base models …

In machine learning, two approaches outperform traditional algorithms: ensemble learning
and deep learning. The former refers to methods that integrate multiple base models in the …

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

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

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 …

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 …