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However, the computational time and memory constraints associated with almost all-learners-based ensembles necessitate efficient approaches. Ensemble pruning, a crucial step, involves selecting a subset of base learners to address these limitations. This study underscores the significance of optimization-based methods in ensemble pruning, with a specific focus on metaheuristics as high-level problem-solving techniques. It reviews the intersection of ensemble learning and metaheuristics, specifically in the context of selective ensembles, marking a unique contribution in this direction of research. Through categorizing metaheuristic-based selective ensembles, identifying their frequently used algorithms and software programs, and highlighting their uses across diverse application domains, this research serves as a comprehensive resource for researchers and offers insights into recent developments and applications. Also, by addressing pivotal research gaps, the study identifies exploring selective ensemble techniques for cluster analysis, investigating cutting-edge metaheuristics and hybrid multi-class models, and optimizing ensemble size as well as hyper-parameters within metaheuristic iterations as prospective research directions. These directions offer a robust roadmap for advancing the understanding and application of metaheuristic-based selective ensembles.<\/jats:p>","DOI":"10.1007\/s00521-024-10203-4","type":"journal-article","created":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T10:01:49Z","timestamp":1723370509000},"page":"17931-17959","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Metaheuristic-based ensemble learning: an extensive review of methods and applications"],"prefix":"10.1007","volume":"36","author":[{"given":"Sahar Saeed","family":"Rezk","sequence":"first","affiliation":[]},{"given":"Kamal Samy","family":"Selim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,11]]},"reference":[{"key":"10203_CR1","doi-asserted-by":"crossref","first-page":"5317","DOI":"10.1007\/978-0-387-30440-3_315","volume-title":"Encyclopedia of complexity and systems science","author":"S D\u017eeroski","year":"2009","unstructured":"D\u017eeroski S, Panov P, \u017denko B (2009) Machine learning, ensemble methods in. 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