The DOI matches its Crossref record; 203 of the 203 references it cites were found in a registry or an index.
A comprehensive review on ensemble deep learning: Opportunities and challenges
Open accessAbstract
In machine learning, two approaches outperform traditional algorithms: ensemble learning and deep learning. The former refers to methods that integrate multiple base models in the same framework to obtain a stronger model that outperforms them. The success of an ensemble method depends on several factors, including how the baseline models are trained and how they are combined. In the literature, there are common approaches to building an ensemble model successfully applied in several domains. On the other hand, deep learning-based models have improved the predictive accuracy of machine learning across a wide range of domains. Despite the diversity of deep learning architectures and their ability to deal with complex problems and the ability to extract features automatically, the main challenge in deep learning is that it requires a lot of expertise and experience to tune the optimal hyper-parameters, which makes it a tedious and time-consuming task. Numerous recent research efforts have been made to approach ensemble learning to deep learning to overcome this challenge. Most of these efforts focus on simple ensemble methods that have some limitations. Hence, this review paper provides comprehensive reviews of the various strategies for ensemble learning, especially in the case of deep learning. Also, it explains in detail the various features or factors that influence the success of ensemble methods. In addition, it presents and accurately categorized several research efforts that used ensemble learning in a wide range of domains.
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“ES demonstrated to improve classification performances in a wide range of tasks from various domains (Mohammed & Kora, 2023), and in many cases they perform better than more traditional methods when dealing with imbalanced classification tasks.”
From CiteFusion: an ensemble framework for citation intent classification harnessing dual-model binary couples and SHAP analyses (2025) · cites this paper as Mohammed & Kora, 2023
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1 retracted
Khamparia, A., Singh, A. D., Anand, D., Gupta, D. K., Khanna, A. K., Arunkumar, N., & Tan, J. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3896-0 · Cited by 67
RetractedBreiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Proceedings of the 24th international conference on Machine learning
(2007) · doi:10.1145/1273496 · Cited by 11,869
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Deep learning in agriculture: A survey
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.02.016 · Cited by 5,886
A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
Yu, Y., Si, X., Hu, C., & Zhang, J. (2019) · Neural Computation · doi:10.1162/neco_a_01199 · Cited by 5,870
Research works printed but not linked: 12
- Freund, Y., Schapire, R.E., et al. 1996. Experiments with a new boosting algorithm. 96, pp. 148–156.
- Garofolo, 1993, Darpa timit acoustic-phonetic continous speech corpus cd-rom. nist speech disc 1–1.1, NASA STI/Recon Technical Report N, 93, 27403
- Go, A., Bhayani, R., Huang, L., 2009. Twitter sentiment classification using distant supervision. CS224N project report, Stanford, vol. 1, no. 12, p. 2009.
- Hospedales, 2021, Meta-learning in neural networks: A survey, IEEE Trans. Pattern Anal. Machine Intell., 44, 5149
- Kuruvayil, 2022, Emotion recognition from facial images with simultaneous occlusion, pose and illumination variations using meta-learning, J. King Saud Univ.-Comput. Informat. Sci., 34, 7271
- Lakshminarayanan, 2017, Simple and scalable predictive uncertainty estimation using deep ensembles, Adv. Neural Informat. Process. Syst., 30
- Li, S, Lee, S.Y., Chen, Y., Huang, C.-R., Zhou, G., 2010. Sentiment classification and polarity shifting. In: Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), pp. 635–643.
- Li, F.H., Huang, M., Yang, Y., Zhu, X., 2011. Learning to identify review spam. In: Twenty-second International Joint Conference on Artificial Intelligence.
- Li, 2012, Heterogeneous ensemble learning for chinese sentiment classification, J. Informat. Comput. Sci., 9, 4551
- Liu, Z., Luo, P., Qiu, S., Wang, X., Tang, X., Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1096–1104.
- Lu, 2017, Ensemble sampling, Adv. Neural Informat. Process. Syst., 30
- Tratz, S., Briesch, D., Laoudi, J., Voss, C., Tweet conversation annotation tool with a focus on an arabic dialect, moroccan darija. In: Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, pp. 135–139.
Cited by
Sorted by citations
Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses
Imamoglu, E. (2024) · Bioengineering · doi:10.3390/bioengineering11111143 · Cited by 143
Du, X., Novoa-Laurentiev, J., Plasek, J. M., Chuang, Y.-W., Wang, L., Marshall, G. A., Mueller, S. K., Chang, F., Datta, S., Paek, H., et al. (2024) · eBioMedicine · doi:10.1016/j.ebiom.2024.105401 · Cited by 69
Paolini, L., Vahdati, S., Di Iorio, A., Wardenga, R., Heibi, I., & Peroni, S. (2025) · Scientometrics · doi:10.1007/s11192-025-05418-8 · Cited by 8
“An interesting research direction in classification tasks is related to ensemble strategies (ES), in which a set of weak - or baseline - learners are employed by means of an aggregation function to produce a single output through the different predictions of the base models (Mohammed & Kora, 2023).”
Metadata
- Authors
- Mohammed, A., & Kora, R.
- Affiliations
- Cairo University (EG)
- Corresponding author
- Mohammed, Ammar
- Kora, Rania
- Venue
- Journal of King Saud University - Computer and Information Sciences
- Published
- 2023-02
- Volume
- 35
- Issue
- 2
- Pages
- 757-774
- ISSN
- 1319-1578 (ISSN-L)
- OpenAlex
- W4318677156
- Cited by
- 1,127
- References
- 203
- Citations by year
- 2026: 238 · 2025: 443 · 2024: 357 · 2023: 88
- FWCI
- 105.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of King Saud University - Computer and Information Sciences (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- ensemble deep learning, ensemble learning, deep learning, hyperparameter optimization, deep learning architectures
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