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.

https://doi.org/10.48550/arXiv.2104.02395

Ganaie, M. A., and Minghui Hu. "Ensemble deep learning: A review." arXiv preprint arXiv:2104.02395 (2021)

Ivo Nowak

HAW Hamburg