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

作者：Ye Ren, Le Zhang, Ponnuthurai Nagaratnam Suganthan · 发表于：IEEE Computational Intelligence Magazine · 年份：2016 · DOI：10.1109/mci.2015.2471235 · 被引用次数：680 · 研究领域：Anomaly Detection Techniques and Applications、Energy Load and Power Forecasting、Data Stream Mining Techniques

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.