Out-of-bag estimation of the optimal sample size in bagging

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How researchers see this paper

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

Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020, background): “5 in order to maximize the diversity of the predictor pool [55].”
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].”
ForEx++: A New Framework for Knowledge Discovery from Decision Forests (2017, methodology): “Bootstrap samples are generally used for inducing diversity among the base classifiers (Martínez-Muñoz and Suárez (2010), Quinlan (1996a)).”
Hyperparameters and tuning strategies for random forest (2018, background): “Martínez-Muñoz and Suárez (2010) carried out an empirical analysis of the dependence of the performance on the sample size.”