Ensemble Learning

By Jitendra Kumar, Ashutosh Kumar Singh, Anand Mohan, Rajkumar Buyya. Encyclopedia of Biometrics, 2019

2,525 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1c3677364ea592629848ba2c6bd087f22cc579ea

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Citations in the corpus

A Survey on ensemble learning under the era of deep learning (2021, background): “It is generally believed that the base learners used for ensemble should have sufficient diversity and the prediction of a single base learner should be as accurate as possible (Granitto et al. 2005; Zhou 2009, 2012).”
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, methodology): “Some of the popular methods are bagging, boosting, majority voting and stacking [117].”
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, methodology): “Ensemble learning strategically combines classifiers or expert models to address problems such as regression and classification [47, 48].”
Towards Making Unlabeled Data Never Hurt (2011, neutral): “(4) becomes max y max t t s. t. t c 0 t y þ d t ; 8t ¼ 1; . . . ; T ; y 2 f AE 1g u : (7”
Data Distillation for Controlling Specificity in Dialogue Generation (2017, methodology): “The system we propose is also related to data manipulation strategies such as boosting (Breiman, 1996b), a type of ensemble method (Dietterich, 2002; Zhou et al., 2002; Krogh et al., 1995) that uses subsets of the original data to produce a series of models and then ”boosts” their performance by…”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, background): “While a single machine learning model may be weak, ensemble methods [137] such as stacking and voting can be”
Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images (2019, methodology): “We can also combine those CPGGAN-generated images, trained without/with additional brain images, similarly to ensemble learning [33].”
A comprehensive review on ensemble deep learning: Opportunities and challenges (2023, background): “Based on a lot of research in machine learning, two methods dominate learning algorithms; namely deep learning (Deng et al., 2014) and ensemble learning (Polikar, 2012; Sagi and Rokach, 2018; Rokach, 2019).”
A Survey on Ensemble Learning for Data Stream Classification (2017, background): “Works addressing ensembles for batch learning environments are also abundant [Kuncheva 2004b; Brown et al. 2005; Polikar 2006; Sewell 2008; Rokach 2009; 2010; Zhou 2012; Wo´zniak et al. 2014], however conclusions in these works are made assuming that learning is performed on static datasets.”
Ensemble learning: A survey (2018, background): “There are several reasons why ensemble methods often improve predictive performance (Dietterich, 2002; Polikar, 2006): (cid:129) Overfitting avoidance : When just a small amount of data is available, a learning algorithm is prone to finding many different hypothesis that predict all of the training…”