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

By Ye Ren, Le Zhang, Ponnuthurai Nagaratnam Suganthan. IEEE Computational Intelligence Magazine, 2016

592 citations recorded by the source corpus.

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A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects (2022, background): “…researchers have conducted some general ensemble learning reviews [31], [32], [33], reviews that focus on ensemble learning for feature selection [34], reviews on ensemble learning in healthcare applications [35], and a review of recent developments and applications of ensem-75 ble learning [36].”
Ensemble deep learning: A review (2021, methodology): “Review of both the classification and regression models was given in [23].”
A survey of randomized algorithms for training neural networks (2016, background): “It is widely accepted that ensemble methods can benefit from low bias and high variance learner [63].”
When Gaussian Process Meets Big Data: A Review of Scalable GPs (2018, background): “(13)This topic has been studied in a broad regime, called ensemble learning [128], [129], for aggregating various learning models to boost predictions.”
Training Multi-Speaker Neural Text-to-Speech Systems using Speaker-Imbalanced Speech Corpora (2019, methodology): “We also intend to introduce diversity to the network structure along with diversity in training data in order to capitalize on the strengths and reduce the weaknesses of different network structures [22].”