Stream-based active learning for sliding windows under the influence of verification latency

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dc.date.accessioned2022-08-16T09:17:44Z
dc.date.available2022-08-16T09:17:44Z
dc.date.issued2021-11-18
dc.description.sponsorshipGefördert im Rahmen des Projekts DEALger
dc.identifierdoi:10.17170/kobra-202206016277
dc.identifier.urihttp://hdl.handle.net/123456789/14057
dc.language.isoeng
dc.relation.doidoi:10.1007/s10994-021-06099-z
dc.rightsNamensnennung 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectclassificationeng
dc.subjectactive learningeng
dc.subjectevolving data streamseng
dc.subjectconcept drifteng
dc.subjectverification latencyeng
dc.subjectlabel delayeng
dc.subject.ddc004
dc.subject.swdAktives Maschinelles Lernenger
dc.subject.swdDatenstromger
dc.subject.swdKlassifikationger
dc.subject.swdSimulationger
dc.subject.swdEtikettierenger
dc.subject.swdAlgorithmusger
dc.titleStream-based active learning for sliding windows under the influence of verification latencyeng
dc.typeAufsatz
dc.type.versionpublishedVersion
dcterms.abstractStream-based active learning (AL) strategies minimize the labeling effort by querying labels that improve the classifier’s performance the most. So far, these strategies neglect the fact that an oracle or expert requires time to provide a queried label. We show that existing AL methods deteriorate or even fail under the influence of such verification latency. The problem with these methods is that they estimate a label’s utility on the currently available labeled data. However, when this label would arrive, some of the current data may have gotten outdated and new labels have arrived. In this article, we propose to simulate the available data at the time when the label would arrive. Therefore, our method Forgetting and Simulating (FS) forgets outdated information and simulates the delayed labels to get more realistic utility estimates. We assume to know the label’s arrival date a priori and the classifier’s training data to be bounded by a sliding window. Our extensive experiments show that FS improves stream-based AL strategies in settings with both, constant and variable verification latency.eng
dcterms.accessRightsopen access
dcterms.creatorPham, Tuan
dcterms.creatorKottke, Daniel
dcterms.creatorKrempl, Georg
dcterms.creatorSick, Bernhard
dcterms.source.identifiereissn:1573-0565
dcterms.source.issueIssue 6
dcterms.source.journalMachine Learningeng
dcterms.source.pageinfo2011-2036
dcterms.source.volumeVolume 111
kup.iskupfalse

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