Bundling classifiers by bagging trees

DOI10.1016/j.csda.2004.06.019zbMath1429.62246OpenAlexW2004005279MaRDI QIDQ957281

Berthold Lausen, Torsten Hothorn

Publication date: 26 November 2008

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

Full work available at URL: https://doi.org/10.1016/j.csda.2004.06.019

zbMATH Keywords

baggingensemble-methodserror rate estimationmethod selection

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (16)

Fusing vantage point trees and linear discriminants for fast feature classification ⋮ Accurate ensemble pruning with PL-bagging ⋮ Ensemble of a subset of \(k\)NN classifiers ⋮ Double-bagging: Combining classifiers by bootstrap aggregation ⋮ Ensemble classification of paired data ⋮ Cross-validated bagged learning ⋮ Generalised indirect classifiers ⋮ Bootstrap estimated true and false positive rates and ROC curve ⋮ Using boosting to prune double-bagging ensembles ⋮ Taxonomy for characterizing ensemble methods in classification tasks: a review and annotated bibliography ⋮ Out-of-Bag Estimation of the Optimal Hyperparameter in SubBag Ensemble Method ⋮ Ensemble classification based on generalized additive models ⋮ ESTIMATING A PARAMETER WHEN IT IS KNOWN THAT THE PARAMETER EXCEEDS A GIVEN VALUE ⋮ A weight-adjusted voting algorithm for ensembles of classifiers ⋮ Aggregating classifiers via Rademacher–Walsh polynomials ⋮ Trimmed bagging

Uses Software

• R
• UCI-ml
• mlbench
• randomForest

Cites Work

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• Bagging predictors
• A consistent combined classification rule
• Double-bagging: Combining classifiers by bootstrap aggregation
• Statistical modeling: The two cultures. (With comments and a rejoinder).
• An almost surely optimal combined classification rule
• Using \(k\)-nearest-neighbor classification in the leaves of a tree
• Arcing classifiers. (With discussion)
• Multivariate tests based on left-spherically distributed linear scores
• Analyzing bagging
• Stacked regressions
• Combining Estiamates in Regression and Classification
• Improvements on Cross-Validation: The .632+ Bootstrap Method
• Adaptive Regression by Mixing
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• Combining Classifiers via Discretization
• New Tests for Data with an Inherent Structure
• Random forests
• Estimating generalization error on two-class datasets using out-of-bag estimates

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