Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]

TLDR

Ensemble methods combine multiple models to improve performance and are applied across fields such as computational intelligence, statistics, and machine learning. The paper reviews traditional and state‑of‑the‑art ensemble methods to provide an extensive summary for practitioners and beginners, and offers recommendations for future research. The review categorizes ensemble methods into conventional, decomposition, negative correlation, multi‑objective optimization, fuzzy, multiple kernel learning, and deep learning approaches, and discusses their variations, improvements, and typical applications.

Abstract

Ensemble methods use multiple models to get better performance. Ensemble methods have been used in multiple research fields such as computational intelligence, statistics and machine learning. This paper reviews traditional as well as state-of-the-art ensemble methods and thus can serve as an extensive summary for practitioners and beginners. The ensemble methods are categorized into conventional ensemble methods such as bagging, boosting and random forest, decomposition methods, negative correlation learning methods, multi-objective optimization based ensemble methods, fuzzy ensemble methods, multiple kernel learning ensemble methods and deep learning based ensemble methods. Variations, improvements and typical applications are discussed. Finally this paper gives some recommendations for future research directions.