Ensemble learning

Ensemble forecasting

Computer science

Boosting (machine learning)

Mathematics

Mathematical analysis

Artificial intelligence

Deep learning

Generalization

Machine learning

Engineering Applications of Artificial Intelligence

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 categorized 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. • This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. • The categorizes of deep ensemble models discussed are bagging, boosting and stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous/heterogeneous ensemble, decision fusion strategies, unsupervised, semi supervised, reinforcement learning and online/incremental, multilabel based deep ensemble models. • Application of deep ensemble models in different domains are discussed. • Finally, this paper provides an outlook towards future research directions.

Ensemble deep learning: A review