Linganisha mbinu

	Robust Bagging	Msitu Imara wa Misitu
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1996–2000s	2000s–2010s
Mwanzilishi	Breiman, L. (bagging); robust variants developed by various authors in 2000s	Various (extensions of Breiman 2001 Random Forest)
Aina	Ensemble (robust bootstrap aggregating)	Robust Ensemble (noise-tolerant bagging of decision trees)
Chanzo asilia	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Chen, S., & Guestrin, C. (2019). Robust Random Forest. In Proceedings of the 36th International Conference on Machine Learning (ICML). Also see: Gao, W., & Zhou, Z.-H. (2013). On the Doubt about Margin Explanation of Boosting. Artificial Intelligence, 203, 1–18.
Majina mbadala	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing	RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest
Zinazohusiana	6	6
Muhtasari	Robust Bagging extends the classic Bootstrap Aggregating (Bagging) framework by replacing or augmenting standard base learners with robust estimators — or by using robust aggregation rules — so that the ensemble remains accurate even when training data contain outliers, mislabelled instances, or heavy-tailed noise distributions.	Robust Random Forest extends the standard Random Forest ensemble by incorporating mechanisms that reduce the influence of outliers, label noise, and corrupted observations. Rather than treating all training instances equally, it applies weighting or filtering strategies so that noisy or anomalous samples contribute less to individual tree splits, yielding predictions that remain reliable even when data quality is imperfect.