Accurate ensemble pruning with PL-bagging
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Publication:1623764
DOI10.1016/j.csda.2014.09.003OpenAlexW1982712747MaRDI QIDQ1623764
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
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)
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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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