*Editorial* **Special Issue on Ensemble Learning and Applications**

### **Panagiotis Pintelas \* and Ioannis E. Livieris**

Department of Mathematics, University of Patras, 265-00 GR Patras, Greece; livieris@upatras.gr **\*** Correspondence: ppintelas@gmail.com

Received: 5 June 2020; Accepted: 9 June 2020; Published: 11 June 2020

**Abstract:** 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. Machine learning ensemble methods combine multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Combining multiple learning models has been theoretically and experimentally shown to provide significantly better performance than their single base learners. In the literature, ensemble learning algorithms constitute a dominant and state-of-the-art approach for obtaining maximum performance, thus they have been applied in a variety of real-world problems ranging from face and emotion recognition through text classification and medical diagnosis to financial forecasting.

**Keywords:** ensemble learning; homogeneous and heterogeneous ensembles; fusion strategies; voting schemes; model combination; black, white and gray box models; incremental and evolving learning
