articleIEEE Computational Intelligence MagazineJan 13, 2016Closed access

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

Nanyang Technological University

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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…

Citation impact

667
total citations
FWCI
81.73
Percentile
100%
References
212
Citations per year

Authors

3

Topics & keywords

Keywords
  • Ensemble learning
  • Boosting (machine learning)
  • Random forest
  • Computer science
  • Artificial intelligence
  • Machine learning
  • Ensemble forecasting
  • Kernel (algebra)
UN Sustainable Development Goals
  • Life in Land
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