Bagging, Boosting and Ensemble Methods

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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.

Ensemble approaches for regression: A survey (2012, methodology): “This is the case of subagging, which obtains each base model using a random subset of the examples [Buhlmann 2010].”
Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects (2021, methodology): “For a review of Bagging and Boosting methods, see Hastie, Tibshirani, and Friedman (2001) and Bühlmann (2012).”
Fake News Detection Using Machine Learning Ensemble Methods (2020, background): “…algorithms using various ensemble methods that are not thoroughly explored in the current literature. ensemble learners have proven to be useful in a wide variety of applications, as the learning models have the tendency to reduce error rate by using techniques such as bagging and boosting [22].”
M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation (2024, neutral): “Particularly, based on the principle of ensemble learning (B¨uhlmann, 2012), the predictions from different retrieval methods can be integrated as a more accurate relevance score given their heterogeneous nature.”
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control (2021, neutral): “A range of ensembling methods exist, such as bagging (bootstrap aggregation) [25], bragging (robust bagging) [69, 70], and boosting [71, 72].”