Read the original document by opening this link in a new tab.
Table of Contents
1. Introduction
2. Research Methodology
3. Theory
4. Ensemble Strategies
4.1. Bagging
4.2. Boosting
4.3. Stacking
5. Conclusion and Future Work
Summary
Ensemble learning combines several individual models to obtain better generalization performance. Deep ensemble learning models combine the advantages of deep learning models and ensemble learning for improved generalization performance. This paper reviews the state-of-the-art deep ensemble models, categorizes them into different types, discusses their applications, and outlines potential future research directions.