Speeding up algorithm selection using average ranking and active testing by introducing runtime
type: Item
instance of: scholarly article
title: Speeding up algorithm selection using average ranking and active testing by introducing runtime
zbMATH Open document ID: 1462.62138
author: Salisu Mamman Abdulrahman
author: Pavel Brazdil
author: Jan N. van Rijn
author: Joaquin Vanschoren
published in: Machine Learning
publication date: 2018-04-03T00:00:00Z
Mathematics Subject Classification ID: 62F07
Mathematics Subject Classification ID: 68T20
zbMATH DE Number: 6855213
zbMATH Keywords: algorithm selection
zbMATH Keywords: meta-learning
zbMATH Keywords: ranking of algorithms
zbMATH Keywords: average ranking
zbMATH Keywords: active testing
zbMATH Keywords: loss curves
zbMATH Keywords: mean interval loss
Wikidata QID: Q113959072
describes a project that uses: RankAggreg
describes a project that uses: OpenML
describes a project that uses: WEKA
MaRDI profile type: Publication
full work available at URL: s10994-017-5687-8
OpenAlex ID: W2768408766
cites work: Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results
cites work: Metalearning
cites work: Q3093335
cites work: Complexity Measures for Meta-learning and Their Optimality
cites work: Pairwise meta-rules for better meta-learning-based algorithm ranking
cites work: Q5312442
DOI: 10.1007/S10994-017-5687-8
description: scientific article; zbMATH DE number 6855213