Algorithm selection on a meta level

DOI10.1007/s10994-022-06161-4arXiv2107.09414OpenAlexW3186287218MaRDI QIDQ6106469

Marcel Wever, Eyke Hüllermeier, Tanja Tornede, Lukas Gehring, Alexander Tornede

Publication date: 27 June 2023

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2107.09414

zbMATH Keywords

baggingboostingstackingalgorithm selectionensemble learningmeta learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• Bagging predictors
• Multi-class AdaBoost
• ASlib: a benchmark library for algorithm selection
• Collective-agreement-based pruning of ensembles
• The algorithm selection competitions 2015 and 2017
• Learning from imprecise and fuzzy observations: data disambiguation through generalized loss minimization
• SUNNY: a Lazy Portfolio Approach for Constraint Solving
• 10.1162/153244303322753616
• Ordering by weighted number of wins gives a good ranking for weighted tournaments
• Unnamed Item

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