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 …

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

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 …

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 …

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 …

Due to the dominant position of deep learning (mostly deep neural networks) in various
artificial intelligence applications, recently, ensemble learning based on deep neural …

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

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 …