Double-bagging: Combining classifiers by bootstrap aggregation

DOI10.1016/S0031-3203(02)00169-3zbMath1028.68144OpenAlexW2084481407MaRDI QIDQ1402702

Berthold Lausen, Torsten Hothorn

Publication date: 28 August 2003

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0031-3203(02)00169-3

zbMATH Keywords

ClassificationDiscriminant analysisBaggingError rate estimationMethod selection bias

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (14)

Accurate ensemble pruning with PL-bagging ⋮ Ensemble of a subset of \(k\)NN classifiers ⋮ A bootstrap-based aggregate classifier for model-based clustering ⋮ Ensemble classification of paired data ⋮ Generalised indirect classifiers ⋮ Bundling classifiers by bagging trees ⋮ Using boosting to prune double-bagging ensembles ⋮ Canonical forest ⋮ Navigating random forests and related advances in algorithmic modeling ⋮ Aggregating classifiers with ordinal response structure ⋮ Oblique random survival forests ⋮ Ensemble of optimal trees, random forest and random projection ensemble classification ⋮ Out-of-Bag Estimation of the Optimal Hyperparameter in SubBag Ensemble Method ⋮ A weight-adjusted voting algorithm for ensembles of classifiers

Uses Software

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• rpart
• ipred

Cites Work

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• Bagging predictors
• Evaluating the effect of optimized cutoff values in the assessment of prognostic factors
• Bundling classifiers by bagging trees
• An efficient method to estimate bagging's generalization error
• Arcing classifiers. (With discussion)
• Boosting the margin: a new explanation for the effectiveness of voting methods
• Combining Estiamates in Regression and Classification
• Improvements on Cross-Validation: The .632+ Bootstrap Method
• A statistical unified framework for rank-based multiple classifier decision combination
• A direct LDA algorithm for high-dimensional data -- with application to face recognition
• Dynamic classifier selection based on multiple classifier behaviour
• Decision templates for multiple classifier fusion: An experimental comparison
• A linear constrained distance-based discriminant analysis for hyperspectral classification

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