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dc.contributor.authorHämäläinen, Joonas
dc.contributor.authorNieminen, Paavo
dc.contributor.authorKärkkäinen, Tommi
dc.date.accessioned2022-01-13T08:01:38Z
dc.date.available2022-01-13T08:01:38Z
dc.date.issued2021
dc.identifier.citationHämäläinen, J., Nieminen, P., & Kärkkäinen, T. (2021). Instance-Based Multi-Label Classification via Multi-Target Distance Regression. In <i>ESANN 2021 : Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Online event (Bruges, Belgium), October 06 - 08</i> (pp. 653-658). ESANN. <a href="https://doi.org/10.14428/esann/2021.ES2021-104" target="_blank">https://doi.org/10.14428/esann/2021.ES2021-104</a>
dc.identifier.otherCONVID_103547139
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/79349
dc.description.abstractInterest in multi-target regression and multi-label classification techniques and their applications have been increasing lately. Here, we use the distance-based supervised method, minimal learning machine (MLM), as a base model for multi-label classification. We also propose and test a hybridization of unsupervised and supervised techniques, where prototype-based clustering is used to reduce both the training time and the overall model complexity. In computational experiments, competitive or improved quality of the obtained models compared to the state-of-the-art techniques was observed.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherESANN
dc.relation.ispartofESANN 2021 : Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Online event (Bruges, Belgium), October 06 - 08
dc.rightsIn Copyright
dc.subject.othermulti-target regression
dc.subject.othermulti-label classification techniques
dc.subject.otherminimal learning machine
dc.titleInstance-Based Multi-Label Classification via Multi-Target Distance Regression
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202201131124
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-2-87587-082-7
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange653-658
dc.type.versionacceptedVersion
dc.rights.copyright© Authors, 2021
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
dc.relation.grantnumber311877
dc.relation.grantnumber315550
dc.subject.ysokoneoppiminen
dc.subject.ysotekoäly
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p2616
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.14428/esann/2021.ES2021-104
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramResearch profiles, AoFen
jyx.fundingprogramAcademy Programme, AoFen
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramAkatemiaohjelma, SAfi
jyx.fundinginformationThe work has been supported by the Academy of Finland from the projects 311877 and 315550.
dc.type.okmA4


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