A comprehensive review on ensemble deep learning: Opportunities and challenges

🤖 Plain-English Summary

In machine learning, two approaches outperform traditional algorithms: ensemble learning and deep learning. Also, it explains in detail the various features or factors that influence the success of ensemble methods.

🔑 Key Findings

• 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.

🔗 Related Resources