The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Ensemble deep learning: A review
Open accessAbstract
Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning architectures are showing better performance compared to the shallow or traditional models. Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorised into bagging, boosting, stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous/heterogeneous ensemble, decision fusion strategies based deep ensemble models. Applications of deep ensemble models in different domains are also briefly discussed. Finally, we conclude this paper with some potential future research directions.
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Cites
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Recent advances and applications of deep learning methods in materials science
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Forecast combinations: An over 50-year review
Wang, X., Hyndman, R. J., Li, F., & Kang, Y. (2023) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2022.11.005 · Cited by 280
Internet of Things attack detection using hybrid Deep Learning Model
Sahu, A. K., Sharma, S., Tanveer, M., & Raja, R. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.05.024 · Cited by 258
Self-citationApplications of deep learning in precision weed management: A review
Rai, N., Zhang, Y., Ram, B. G., Schumacher, L., Yellavajjala, R. K., Bajwa, S., & Sun, X. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.107698 · Cited by 218
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Metadata
- Authors
- Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N.
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2022-10
- Volume
- 115
- Pages
- 105151
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W3149839747
- Cited by
- 2,143
- References
- 21
- FWCI
- 143.31
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2022.105151
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