Ensemble learning: A survey

By Omer Sagi, Lior Rokach. WIREs Data Mining Knowl. Discov., 2018

3,015 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c99179ca3784e3465fd9ed049d7f34b50d39393e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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In Signal Processing

Citations in the corpus

Machine learning and deep learning—A review for ecologists (2022, neutral): “For example, bagging decreases the similarity between ensemble members by bootstrapping the data (Sagi & Rokach, 2018 ).”
Machine Learning in Agriculture: A Comprehensive Updated Review (2021, background): “performance would be superior comparing to a single inducer [89].”
Ensemble deep learning: A review (2021, methodology): “Comprehensive review of the ensemble methods and the challenges were given in [24].”
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…”
CatBoost for big data: an interdisciplinary review (2020, methodology): “Prior to the introduction of CatBoost, Sagi and Rokach published “Ensemble learning: a survey” [42].”
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources (2019, methodology): “Bagging (abbreviation for bootstrap aggregation) is an ensemble learning method [18] proposed in Breiman [63].”
LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion (2023, methodology): “widely employed technique to enhance a model’s capabilities by leveraging multiple weaker models (Sagi and Rokach, 2018; Anioł and Pietroń, 2019; Wang et al., 2016).”
A survey of uncertainty in deep neural networks (2021, methodology): “5) Making Ensemble Methods more Efficient: Compared to single model methods, ensemble methods come along with a significantly increased computational effort and memory consumption [217], [45].”
A Survey on ensemble learning under the era of deep learning (2021, background): “Different from existing review articles (Sagi and Rokach 2018; Dong et al. 2020) that mostly discussed about traditional ensemble learning, reviews (Cao et al.”
CBNet: A Composite Backbone Network Architecture for Object Detection (2021, background): “diction results through post processing [48], [49].”
A comprehensive review on ensemble deep learning: Opportunities and challenges (2023, methodology): “In the literature, some review efforts have introduced the concept of deep ensemble learning(Dong et al., 2020; Sagi and Rokach, 2018).”
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices (2019, methodology): “This work is a generalization of popular ensemble methods, which have been shown to improve predictive power by reducing overfitting, avoiding local optima, and combining representations to achieve optimal hypotheses [36].”
Machine Learning Based Intrusion Detection Systems for IoT Applications (2019, neutral): “Among ensembles, widely studied algorithms [29, 41, 57] like Random forest (RF), AdaBoost (AB), Gradient boosted machine (GBM), Extreme gradient boosting (ETC), and Ex-tremely randomized trees (ETC) are chosen.”
Encoders and Ensembles for Task-Free Continual Learning (2021, methodology): “First, we consider ensemble methods, which have been studied widely within machine learning [39].”
ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs (2023, background): “This has led to the development of ensembles (Sagi and Rokach, 2018) in multimodal learning (Zeng et al., 2023; Li et al., 2022a).”
Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration (2026, unknown): “Large language model (LLM) ensemble, which stems from ensemble learning (Dietterich 2000, Sagi and Rokach 2018), has emerged as a promising research direction that aims to harness the collective wisdom of LLMs to achieve superior performance.”