The DOI matches its Crossref record; 161 of the 161 references it cites were found in a registry or an index.
A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects
Open accessAbstract
Ensemble learning techniques have achieved state-of-the-art performance in diverse machine learning applications by combining the predictions from two or more base models. This paper presents a concise overview of ensemble learning, covering the three main ensemble methods: bagging, boosting, and stacking, their early development to the recent state-of-the-art algorithms. The study focuses on the widely used ensemble algorithms, including random forest, adaptive boosting (AdaBoost), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). An attempt is made to concisely cover their mathematical and algorithmic representations, which is lacking in the existing literature and would be beneficial to machine learning researchers and practitioners.
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Cites
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Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
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Cited by
Sapkota, R., Flores-Calero, M., Qureshi, R., Badgujar, C., Nepal, U., Poulose, A., Zeno, P., Vaddevolu, U. B. P., Khan, S., Shoman, M., et al. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11253-3 · Cited by 308
DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT
Miao, Q., Zheng, W., Lv, Y., Huang, M., Ding, W., & Wang, F.-Y. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123561 · Cited by 78
Alqarni (2024) · doi:10.1016/j.caeai.2024.100205 · Cited by 62
Boosting skin cancer diagnosis accuracy with ensemble approach
Natha, P., Tera, S. P., Chinthaginjala, R., Rab, S. O., Narasimhulu, C. V., & Kim, T. H. (2025) · Scientific Reports · doi:10.1038/s41598-024-84864-5 · Cited by 45
Jiang, Z., & Zhang, Z. (2025) · Research Methods in Applied Linguistics · doi:10.1016/j.rmal.2025.100237 · Cited by 10
Metadata
- Authors
- Mienye, I. D., & Sun, Y.
- Venue
- IEEE Access
- Published
- 2022
- Volume
- 10
- Pages
- 99129-99149
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4296079469
- Cited by
- 1,298
- References
- 161
- FWCI
- 78.20
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
