Speeding up algorithm selection using average ranking and active testing by introducing runtime

DOI10.1007/s10994-017-5687-8zbMath1462.62138OpenAlexW2768408766WikidataQ113959072 ScholiaQ113959072MaRDI QIDQ1707468

Joaquin Vanschoren, Salisu Mamman Abdulrahman, Pavel Brazdil, Jan N. van Rijn

Publication date: 3 April 2018

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

Full work available at URL: https://doi.org/10.1007/s10994-017-5687-8

zbMATH Keywords

algorithm selectionmeta-learningactive testingaverage rankingloss curvesmean interval lossranking of algorithms

Mathematics Subject Classification ID

Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Statistical ranking and selection procedures (62F07)

Related Items (1)

Scalable Gaussian process-based transfer surrogates for hyperparameter optimization

Uses Software

• WEKA
• RankAggreg
• OpenML

Cites Work

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• Pairwise meta-rules for better meta-learning-based algorithm ranking
• Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results
• Complexity Measures for Meta-learning and Their Optimality
• Metalearning

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