An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

By Eric Bauer, Ron Kohavi. 1999

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In Statistical Learning Theory

Citations in the corpus

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time (2022, methodology): “Combining the outputs of many models is a foundational technique for improving the accuracy and robustness of machine learning models (Dietterich, 2000; Bauer and Kohavi, 1999; Breiman, 1996; Friedman et al., 2001; Lakshminarayanan et al., 2017; Freund and Schapire, 1997).”
Random Forests (2001, background): “But none of these these three forests do as well as Adaboost (Freund & Schapire, 1996) or other algorithms that work by adaptive reweighting (arcing) of the training set (see Breiman, 1998b; Dieterrich, 1998; Bauer & Kohavi, 1999).”
Robust fine-tuning of zero-shot models (2021, background): “Traditional ensemble methods, which we refer to as output-space ensembles, combine the predictions (outputs) of many classiﬁers (Dietterich, 2000; Bauer & Kohavi, 1999; Breiman, 1996; Friedman et al., 2001; Lakshminarayanan et al., 2017; Freund & Schapire, 1997).”
A comparison of AdaBoost algorithms for time series forecast combination (2016, result): “This indicates substantial in-sample overfitting by CART compared to MLPs, a novel insight for Boosting on time series data, which in contrast to results obtained in Bagging (Breiman 1996; Bauer and Kohavi 1999).”
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016, methodology): “For classification, there are improved versions of bagging such as online bagging (and boosting) [44], double-bagging [45], wagging [46].”
Intelligible models for classification and regression (2012, methodology): “We use the well-known technique of bagging to reducing variance [6].”
p-Adic mathematical physics: the first 30 years (2017, background): “In computer science clustering with respect to a family of metrics results in a network of clusters (this network is not necessarily a tree) [239, 240].”
Multivariate decision trees using linear discriminants and tabu search (2003, background): “have been reported in a number of studies [2], [45].”
Text Classification Algorithms: A Survey (2019, methodology): “The algorithm is generated by different bootstrap samples [111].”
Ensemble learning: A survey (2018, background): “Wagging (weight aggregation; Bauer & Kohavi, 1999) is a variation that assigns random weights to the instances.”
Ensemble methods and partial least squares regression (2004, methodology): “Keywords ensemble methods; bootstrap aggregating (bagging); data augmentation; noise addition; partial least squares regression (PLSR)”
A study of the behavior of several methods for balancing machine learning training data (2004, result): “Although some research papers state that pruning might be helpful with imbalanced data sets in some circumstances [4], other papers indicate that when target misclassiﬁcation costs or class distributions are unknown, then pruning should be avoided [26; 2].”
Tree Induction for Probability-Based Ranking (2003, methodology): “Our results in this respect agree with, but are stronger than, the results of Bauer and Kohavi (1999) .”