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Special Issue on Ensemble Learning and Applications

Department of Mathematics, University of Patras, 265-00 GR Patras, Greece
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Algorithms 2020, 13(6), 140; https://doi.org/10.3390/a13060140
Received: 5 June 2020 / Accepted: 9 June 2020 / Published: 11 June 2020
(This article belongs to the Special Issue Ensemble Algorithms and Their Applications)
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. View Full-Text
Keywords: ensemble learning; homogeneous and heterogeneous ensembles; fusion strategies; voting schemes; model combination; black, white and gray box models; incremental and evolving learning ensemble learning; homogeneous and heterogeneous ensembles; fusion strategies; voting schemes; model combination; black, white and gray box models; incremental and evolving learning
MDPI and ACS Style

Pintelas, P.; Livieris, I.E. Special Issue on Ensemble Learning and Applications. Algorithms 2020, 13, 140.

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