Cited by 1,127Machine Learning and Data ClassificationAdvanced Neural Network ApplicationsMachine Learning and Algorithms

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A comprehensive review on ensemble deep learning: Opportunities and challenges

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Mohammed, Ammar, Kora, Rania·2023·Journal of King Saud University - Computer and Information Sciences

DOI 10.1016/j.jksuci.2023.01.014

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Abstract

In machine learning, two approaches outperform traditional algorithms: ensemble learning and deep learning. The former refers to methods that integrate multiple base models in the same framework to obtain a stronger model that outperforms them. The success of an ensemble method depends on several factors, including how the baseline models are trained and how they are combined. In the literature, there are common approaches to building an ensemble model successfully applied in several domains. On the other hand, deep learning-based models have improved the predictive accuracy of machine learning across a wide range of domains. Despite the diversity of deep learning architectures and their ability to deal with complex problems and the ability to extract features automatically, the main challenge in deep learning is that it requires a lot of expertise and experience to tune the optimal hyper-parameters, which makes it a tedious and time-consuming task. Numerous recent research efforts have been made to approach ensemble learning to deep learning to overcome this challenge. Most of these efforts focus on simple ensemble methods that have some limitations. Hence, this review paper provides comprehensive reviews of the various strategies for ensemble learning, especially in the case of deep learning. Also, it explains in detail the various features or factors that influence the success of ensemble methods. In addition, it presents and accurately categorized several research efforts that used ensemble learning in a wide range of domains.

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“An interesting research direction in classification tasks is related to ensemble strategies (ES), in which a set of weak - or baseline - learners are employed by means of an aggregation function to produce a single output through the different predictions of the base models (Mohammed & Kora, 2023).”

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Authors: Mohammed, A., & Kora, R.
Affiliations: Cairo University (EG)
Corresponding author: Mohammed, Ammar
Kora, Rania
ORCID: Mohammed, Ammar
Kora, Rania
Venue: Journal of King Saud University - Computer and Information Sciences
Publisher: Springer Science and Business Media LLC
Published: 2023-02
Volume: 35
Issue: 2
Pages: 757-774
ISSN: 1319-1578 (ISSN-L)
DOI: 10.1016/j.jksuci.2023.01.014
OpenAlex: W4318677156
Cited by: 1,127
References: 203
Citations by year: 2026: 238 · 2025: 443 · 2024: 357 · 2023: 88
FWCI: 105.71
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Journal of King Saud University - Computer and Information Sciences (publishedVersion, cc-by-nc-nd)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Keywords: ensemble deep learning, ensemble learning, deep learning, hyperparameter optimization, deep learning architectures

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A comprehensive review on ensemble deep learning: Opportunities and challenges

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