Ensemble deep learning: A review

被引：0

作者：

Ganaie, M.A. ^{[1
]}

Hu, Minghui ^{[2
]}

Malik, A.K. ^{[1
]}

Tanveer, M. ^{[1
]}

Suganthan, P.N. ^{[2
,3
]}

机构：

[1] Department of Mathematics, Indian Institute of Technology Indore, Simrol, Indore,453552, India

[2] School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore

[3] KINDI Center for Computing Research College of Engineering, Qatar University, Qatar

来源：

Engineering Applications of Artificial Intelligence | 2022年 / 115卷

关键词：

Deep learning;

D O I：

暂无

中图分类号：

TB18 [人体工程学]; Q98 [人类学];

学科分类号：

030303 ; 1201 ;

摘要：

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. © 2022 Elsevier Ltd

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