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dc.contributor.authorLinja, Joakim
dc.contributor.authorHämäläinen, Joonas
dc.contributor.authorNieminen, Paavo
dc.contributor.authorKärkkäinen, Tommi
dc.date.accessioned2022-11-16T11:01:14Z
dc.date.available2022-11-16T11:01:14Z
dc.date.issued2023
dc.identifier.citationLinja, J., Hämäläinen, J., Nieminen, P., & Kärkkäinen, T. (2023). Feature selection for distance-based regression : An umbrella review and a one-shot wrapper. <i>Neurocomputing</i>, <i>518</i>, 344-359. <a href="https://doi.org/10.1016/j.neucom.2022.11.023" target="_blank">https://doi.org/10.1016/j.neucom.2022.11.023</a>
dc.identifier.otherCONVID_160101260
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/83945
dc.description.abstractFeature selection (FS) may improve the performance, cost-efficiency, and understandability of supervised machine learning models. In this paper, FS for the recently introduced distance-based supervised machine learning model is considered for regression problems. The study is contextualized by first providing an umbrella review (review of reviews) of recent development in the research field. We then propose a saliency-based one-shot wrapper algorithm for FS, which is called MAS-FS. The algorithm is compared with a set of other popular FS algorithms, using a versatile set of simulated and benchmark datasets. Finally, experimental results underline the usefulness of FS for regression, confirming the utility of certain filter algorithms and particularly the proposed wrapper algorithm.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofseriesNeurocomputing
dc.rightsCC BY 4.0
dc.subject.otherdistance-based method
dc.subject.otherfeature selection
dc.subject.otherfeature saliency
dc.subject.otherwrapper algorithm
dc.subject.otherEMLM
dc.titleFeature selection for distance-based regression : An umbrella review and a one-shot wrapper
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202211165239
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTekniikkafi
dc.contributor.oppiaineComputing Education Researchfi
dc.contributor.oppiaineTutkintokoulutusfi
dc.contributor.oppiaineKoulutusteknologia ja kognitiotiedefi
dc.contributor.oppiaineHuman and Machine based Intelligence in Learningfi
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineEngineeringen
dc.contributor.oppiaineComputing Education Researchen
dc.contributor.oppiaineDegree Educationen
dc.contributor.oppiaineLearning and Cognitive Sciencesen
dc.contributor.oppiaineHuman and Machine based Intelligence in Learningen
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange344-359
dc.relation.issn0925-2312
dc.relation.volume518
dc.type.versionpublishedVersion
dc.rights.copyright© 2022 The Authors. Published by Elsevier B.V.
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber351579
dc.relation.grantnumber315550
dc.subject.ysotekoäly
dc.subject.ysoalgoritmit
dc.subject.ysoparantaminen (paremmaksi muuttaminen)
dc.subject.ysokoneoppiminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p2616
jyx.subject.urihttp://www.yso.fi/onto/yso/p14524
jyx.subject.urihttp://www.yso.fi/onto/yso/p4229
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1016/j.neucom.2022.11.023
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramOthers, AoFen
jyx.fundingprogramAcademy Programme, AoFen
jyx.fundingprogramMuut, SAfi
jyx.fundingprogramAkatemiaohjelma, SAfi
jyx.fundinginformationThis work has been supported by the Academy of Finland through the projects 315550 (HNP-AI) and 351579 (MLNovCat). We acknowledge grants of computer capacity from the Finnish Grid and Cloud Infrastructure (FCCI; persistent identifier urn:nbn:fi:research-infras-2016072533).
dc.type.okmA1


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