A survey on ensemble learning

By Xibin Dong, Zhiwen Yu, Wenming Cao, Yifan Shi, Qianli Ma. Frontiers of Computer Science, 2019

1,839 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e1b41796b752d6fb6032bbad2f8998302209d79b

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In Artificial Intelligence

Citations in the corpus

FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply (2020, methodology): “Ensemble methods: A natural approach to exploiting multiple predictors is ensemble methods [25, 29, 45].”
A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects (2022, background): “In the past, 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…”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, neutral): “2(a), another representative learning architecture is ensemble learning [44], which is shown in Fig.”
A comprehensive review on ensemble deep learning: Opportunities and challenges (2023, background): “In the literature, some review efforts have introduced the concept of deep ensemble learning(Dong et al., 2020; Sagi and Rokach, 2018).”
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “There are three main classes of ensemble learning bagging , boosting , and stacking [49, 50].”
Bagging-Based Model Merging for Robust General Text Embeddings (2026, unknown): “Ensemble learning [11, 47] is a powerful machine learning paradigm where multiple models (often referred to as “base learners”) are combined to solve a problem and produce a better performance than individual models.”