OBSERVATIONS ON BAGGING

By Andreas Buja, Werner Stuetzle. 2006

104 citations recorded by the source corpus.

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In Optimization & Training

Citations in the corpus

Analysis of a Random Forests Model (2010, methodology): “This subspace randomization scheme is blended with bagging ([9, 15, 16, 4]) to resample, with replacement, the training data set each time a new individual tree is grown.”
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (2015, background): “As observed by Buja and Stuetzle [2006], Chen and Hall [2003], Friedman and Hall [2007] and others, estimators of this form can exhibit surprising properties even in simple situations; meanwhile, using subsampling rather than bootstrap sampling has been found to avoid several pitfalls [e.g.,…”
3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture (2018, methodology): “After all models from every fold was trained, the models then evaluated the testing data, and the results combined and averaged through bootstrap aggregation (bagging) (Breiman 1996) without replacement (Buja and Stuetzle 2006).”
Bagging, Boosting and Ensemble Methods (2012, background): “The empirical fact that bagging improves the predictive performance of regression and classification trees is nowadays widely documented (Borra and Di Ciaccio 2002; Breiman 1996a,b; Bühlmann and Yu 2002; Buja and Stuetzle 2006).”
Out-of-bag estimation of the optimal sample size in bagging (2010, methodology): “A less explored variant of bagging is m-out-of-n bagging without replacement, also known as subagging [9, 10, 11].”
Asymptotic Theory for Random Forests (2014, background): “As observed by Buja and Stuetzle [10], Chen and Hall [11], Friedman and Hall [16] and others, estimators of this form can exhibit surprising properties even in simple situations.”
Estimation and Accuracy after Model Selection (2013, methodology): “(2.8) Bootstrap smoothing (Efron and Tibshirani 1996), a form of model averaging, is better known as “bagging” in the prediction literature; see Breiman (1996) and Buja and Stuetzle (2006).”
Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles (2019, methodology): “For bootstrap sampling without replacement [61], typically half of the original dataset size (known as half-sampling [40]) is used as the number of out-of-bag samples [62], leading to a well-performing bagging ensemble [63].”