Ensemble deep learning: A review
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Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning models with multilayer processing architecture is showing better performance as compared to the shallow or traditional classification 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. Application of deep ensemble models in different domains is also briefly discussed.
Type:
Scientific Paper
Area:
Machine Learning
Target Group:
Advanced
DOI:
https://doi.org/10.48550/arXiv.2104.02395
Cite as:
Ganaie, M. A., and Minghui Hu. "Ensemble deep learning: A review." arXiv preprint arXiv:2104.02395 (2021)
Author of the review:
Ivo Nowak
HAW Hamburg
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Enzo Ubaldo Petrocco
This paper reviews state-of-art deep ensemble models. The ensemble models are broadly categorized and explained, and then their applications in different domains are briefly discussed.