Trimmed bagging

DOI10.1016/j.csda.2007.06.012zbMath1452.62047OpenAlexW4244459380MaRDI QIDQ1020824

Kristel Joossens, Aurélie Lemmens, Christophe Croux

Publication date: 2 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://lirias.kuleuven.be/handle/123456789/201706

zbMATH Keywords

aggregationdecision treeserror ratebaggingsupport vector machinetrimmed means

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (9)

Accurate ensemble pruning with PL-bagging ⋮ Cost-sensitive business failure prediction when misclassification costs are uncertain: a heterogeneous ensemble selection approach ⋮ Random forest pruning techniques: a recent review ⋮ Taxonomy for characterizing ensemble methods in classification tasks: a review and annotated bibliography ⋮ Pattern recognition via projection-based \(k\)NN rules ⋮ Bagging null space locality preserving discriminant classifiers for face recognition ⋮ Out-of-Bag Estimation of the Optimal Hyperparameter in SubBag Ensemble Method ⋮ Ensemble classification based on generalized additive models ⋮ Editorial: Machine learning and robust data mining

Uses Software

• ElemStatLearn

Cites Work

• Unnamed Item
• Bagging predictors
• Bundling classifiers by bagging trees
• Boosting and instability for regression trees
• Classifying densities using functional regression trees: applications in oceanology
• Arcing classifiers. (With discussion)
• Analyzing bagging
• Estimating the functional form of a continuous covariate's effect on survival time
• The elements of statistical learning. Data mining, inference, and prediction
• Random forests

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