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Ensemble learning for operations research and business analytics

Koen W. De Bock, Matthias Bogaert (UGent) and Philippe du Jardin
(2025) ANNALS OF OPERATIONS RESEARCH. 353(2). p.419-448
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Abstract
This paper introduces the special issue on Ensemble Learning for Operations Research and Business Analytics. Its main purpose is to provide summaries for the 14 contributing research papers that were accepted for inclusion in this special issue. We first define an updated and extended taxonomy of ensemble learner architectures to characterize and differentiate ensemble learning algorithms. Subsequently, we characterize the special issue contributions in two ways: with respect to the operations research (OR) application they address and contribute to, and methodologically with respect to the newly defined taxonomy. Finally, we present an ambitious agenda for future research on ensemble learning for OR and business analytics.
Keywords
Ensemble learning, Machine learning, Business analytics, OR, PREDICTION, CLASSIFIERS, DIVERSITY

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MLA
De Bock, Koen W., et al. “Ensemble Learning for Operations Research and Business Analytics.” ANNALS OF OPERATIONS RESEARCH, vol. 353, no. 2, 2025, pp. 419–48, doi:10.1007/s10479-025-06852-w.
APA
De Bock, K. W., Bogaert, M., & du Jardin, P. (2025). Ensemble learning for operations research and business analytics. ANNALS OF OPERATIONS RESEARCH, 353(2), 419–448. https://doi.org/10.1007/s10479-025-06852-w
Chicago author-date
De Bock, Koen W., Matthias Bogaert, and Philippe du Jardin. 2025. “Ensemble Learning for Operations Research and Business Analytics.” ANNALS OF OPERATIONS RESEARCH 353 (2): 419–48. https://doi.org/10.1007/s10479-025-06852-w.
Chicago author-date (all authors)
De Bock, Koen W., Matthias Bogaert, and Philippe du Jardin. 2025. “Ensemble Learning for Operations Research and Business Analytics.” ANNALS OF OPERATIONS RESEARCH 353 (2): 419–448. doi:10.1007/s10479-025-06852-w.
Vancouver
1.
De Bock KW, Bogaert M, du Jardin P. Ensemble learning for operations research and business analytics. ANNALS OF OPERATIONS RESEARCH. 2025;353(2):419–48.
IEEE
[1]
K. W. De Bock, M. Bogaert, and P. du Jardin, “Ensemble learning for operations research and business analytics,” ANNALS OF OPERATIONS RESEARCH, vol. 353, no. 2, pp. 419–448, 2025.
@article{01K7PQ02M29N235VWJ17656S45,
  abstract     = {{This paper introduces the special issue on Ensemble Learning for Operations Research and Business Analytics. Its main purpose is to provide summaries for the 14 contributing research papers that were accepted for inclusion in this special issue. We first define an updated and extended taxonomy of ensemble learner architectures to characterize and differentiate ensemble learning algorithms. Subsequently, we characterize the special issue contributions in two ways: with respect to the operations research (OR) application they address and contribute to, and methodologically with respect to the newly defined taxonomy. Finally, we present an ambitious agenda for future research on ensemble learning for OR and business analytics.}},
  author       = {{De Bock, Koen W. and Bogaert, Matthias and du Jardin, Philippe}},
  issn         = {{0254-5330}},
  journal      = {{ANNALS OF OPERATIONS RESEARCH}},
  keywords     = {{Ensemble learning,Machine learning,Business analytics,OR,PREDICTION,CLASSIFIERS,DIVERSITY}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{419--448}},
  title        = {{Ensemble learning for operations research and business analytics}},
  url          = {{http://doi.org/10.1007/s10479-025-06852-w}},
  volume       = {{353}},
  year         = {{2025}},
}
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