Ensemble deep learning: A review
- URL: http://arxiv.org/abs/2104.02395v1
- Date: Tue, 6 Apr 2021 09:56:29 GMT
- Title: Ensemble deep learning: A review
- Authors: M.A. Ganaie (1) and Minghui Hu (2) and M. Tanveer*(1) and P.N.
Suganthan*(2) (* Corresponding Author (1) Department of Mathematics, Indian
Institute of Technology Indore, Simrol, Indore, 453552, India (2) School of
Electrical & Electronic Engineering, Nanyang Technological University,
Singapore)
- Abstract summary: Ensemble learning combines several individual models to obtain better generalization performance.
Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning.
This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. The ensemble models are broadly
categorised into ensemble models like 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 is also briefly discussed. Finally, we conclude this paper with some
future recommendations and research directions.
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