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Hyperparameter self-tuning for data streams

2021Journal article Open access
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dc.contributor.authorVeloso, Bruno
dc.contributor.authorGama, João
dc.contributor.authorMalheiro, Benedita
dc.contributor.authorVinagre, João
dc.date.accessioned2021-10-12T15:59:48Z
dc.date.available2021-10-12T15:59:48Z
dc.date.issued2021
dc.description.abstractThe number of Internet of Things devices generating data streams is expected to grow exponentially with the support of emergent technologies such as 5G networks. Therefore, the online processing of these data streams requires the design and development of suitable machine learning algorithms, able to learn online, as data is generated. Like their batch-learning counterparts, stream-based learning algorithms require careful hyperparameter settings. However, this problem is exacerbated in online learning settings, especially with the occurrence of concept drifts, which frequently require the reconfiguration of hyperparameters. In this article, we present SSPT, an extension of the Self Parameter Tuning (SPT) optimisation algorithm for data streams. We apply the Nelder–Mead algorithm to dynamically-sized samples, converging to optimal settings in a single pass over data while using a relatively small number of hyperparameter configurations. In addition, our proposal automatically readjusts hyperparameters when concept drift occurs. To assess the effectiveness of SSPT, the algorithm is evaluated with three different machine learning problems: recommendation, regression, and classification. Experiments with well-known data sets show that the proposed algorithm can outperform previous hyperparameter tuning efforts by human experts. Results also show that SSPT converges significantly faster and presents at least similar accuracy when compared with the previous double-pass version of the SPT algorithm.pt_PT
dc.description.sponsorshipThis work was partially supported by: (i) National Funds through the FCT – Fundação para a Ciência e a Tecnologia, Portugal (Portuguese Foundation for Science and Technology) as part of project UIDB/50014/2020; and (ii) the European Commission funded project “Humane AI: Toward AI Systems That Augment and Empower Humans by Understanding Us, our Society and the World Around Us” (grant #820437). The support is gratefully acknowledged.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.inffus.2021.04.011pt_PT
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/10400.22/18698
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationFundação para a Ciência e Tecnologia UIDB/50014/2020pt_PT
dc.relationEuropean Commission funded project “Humane AI: Toward AI Systems That Augment and Empower Humans by Understanding Us, our Society and the World Around Us” (grant #820437).pt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1566253521000841?via%3Dihub#!pt_PT
dc.subjectData Streamspt_PT
dc.subjectOptimisationpt_PT
dc.subjectHyperparameterspt_PT
dc.titleHyperparameter self-tuning for data streamspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceAmsterdam, Netherlandspt_PT
oaire.citation.endPage86pt_PT
oaire.citation.startPage75pt_PT
oaire.citation.titleInformation Fusionpt_PT
oaire.citation.volume76pt_PT
person.familyNameBENEDITA CAMPOS NEVES MALHEIRO
person.givenNameMARIA
person.identifier.ciencia-id7A15-08FC-4430
person.identifier.orcid0000-0001-9083-4292
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationbabd4fda-654a-4b59-952d-6113eebbb308
relation.isAuthorOfPublication.latestForDiscoverybabd4fda-654a-4b59-952d-6113eebbb308

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