An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, B

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Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches (2013, methodology): “In this situation, we can proceed by transforming the original multiple-class problem into binary subsets, which are easier to discriminate, via a class binarization technique [3,13,25].”
Special Invited Paper-Additive logistic regression: A statistical view of boosting (2000, background): “Note that other committee approaches to classification such as bagging [Breiman (1996)] and randomized trees [Dietterich (1998)] while admitting parallel implementations , cannot take advantage of this approach to reduce computation.”
A comparative analysis of gradient boosting algorithms (2019, unknown): “This study follows the path of many other previous comparative analysis, such as [10,5,8], with the intent of covering a gap related to gradient boosting and its more recent variants XGBoost, LightGBM and CatBoost.”
Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey (2019, background): “It is well known that bagging is more robust to label noise than boosting [175].”
Ensemble Methods in Machine Learning (2007, methodology): “The largest of these are the studies by Bauer and Kohavi (1999) and by Dietterich (2000).”