Accurate ensemble pruning with PL-bagging

DOI10.1016/j.csda.2014.09.003OpenAlexW1982712747MaRDI QIDQ1623764

Dongjun Chung, Hyunjoong Kim

Publication date: 23 November 2018

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

Full work available at URL: https://doi.org/10.1016/j.csda.2014.09.003

zbMATH Keywords

classificationbaggingboostingLassorandom forestensemble

Mathematics Subject Classification ID

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

Related Items (2)

Random forest pruning techniques: a recent review ⋮ Pruning variable selection ensembles

Uses Software

• R
• UCI-ml

Cites Work

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• Bagging predictors
• A weight-adjusted voting algorithm for ensembles of classifiers
• Bundling classifiers by bagging trees
• Using boosting to prune double-bagging ensembles
• Improving the precision of classification trees
• Trimmed bagging
• A decision-theoretic generalization of on-line learning and an application to boosting
• Double-bagging: Combining classifiers by bootstrap aggregation
• Least angle regression. (With discussion)
• Random forests

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