{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T11:32:20Z","timestamp":1790422340641,"version":"4.1.0"},"reference-count":12,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,6,11]],"date-time":"2020-06-11T00:00:00Z","timestamp":1591833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/a13060140","type":"journal-article","created":{"date-parts":[[2020,6,12]],"date-time":"2020-06-12T05:02:24Z","timestamp":1591938144000},"page":"140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Special Issue on Ensemble Learning and Applications"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8436-2743","authenticated-orcid":false,"given":"Panagiotis","family":"Pintelas","sequence":"first","affiliation":[{"name":"Department of Mathematics, University of Patras, 265-00 GR Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3996-3301","authenticated-orcid":false,"given":"Ioannis E.","family":"Livieris","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Patras, 265-00 GR Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Brown, G. (2010). Ensemble Learning. Encyclopedia of Machine Learning, Springer.","DOI":"10.1007\/978-0-387-30164-8_252"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Polikar, R. (2012). Ensemble learning. Ensemble Machine Learning, Springer.","DOI":"10.1007\/978-1-4419-9326-7_1"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, C., and Ma, Y. (2012). Ensemble Machine Learning: Methods and Applications, Springer.","DOI":"10.1007\/978-1-4419-9326-7"},{"key":"ref_4","first-page":"110","article-title":"Ensemble learning","volume":"Volume 2","author":"Dietterich","year":"2002","journal-title":"The Handbook of Brain Theory and Neural Networks"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Livieris, I.E., Kanavos, A., Tampakas, V., and Pintelas, P. (2019). A weighted voting ensemble self-labeled algorithm for the detection of lung abnormalities from X-rays. 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Algorithms, 13.","DOI":"10.3390\/a13010026"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Demertzis, K., and Iliadis, L. (2020). GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspectral Image Analysis and Classification. Algorithms, 13.","DOI":"10.3390\/a13030061"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zvarevashe, K., and Olugbara, O. (2020). Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition. Algorithms, 13.","DOI":"10.3390\/a13030070"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Haralabopoulos, G., Anagnostopoulos, I., and McAuley, D. (2020). Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content. Algorithms, 13.","DOI":"10.3390\/a13040083"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Livieris, I.E., Pintelas, E., Stavroyiannis, S., and Pintelas, P. (2020). Ensemble Deep Learning Models for Forecasting Cryptocurrency Time-Series. Algorithms, 13.","DOI":"10.3390\/a13050121"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/6\/140\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:37:51Z","timestamp":1760175471000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/6\/140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,11]]},"references-count":12,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["a13060140"],"URL":"https:\/\/doi.org\/10.3390\/a13060140","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,11]]}}}