Binary transformation method for multi-label stream classification

buir.contributor.authorGülcan, Ege Berkay
buir.contributor.authorEcevit, Işın Su
buir.contributor.authorCan, Fazlı
buir.contributor.orcidGülcan, Ege Berkay|0000-0003-1237-0829
buir.contributor.orcidEcevit, Işın Su|0000-0002-1666-785X
buir.contributor.orcidCan, Fazlı|0000-0003-0016-4278
dc.citation.epage3972en_US
dc.citation.spage3968en_US
dc.contributor.authorGülcan, Ege Berkay
dc.contributor.authorEcevit, Işın Su
dc.contributor.authorCan, Fazlı
dc.coverage.spatialAtlanta, United Statesen_US
dc.date.accessioned2023-02-25T11:18:56Z
dc.date.available2023-02-25T11:18:56Z
dc.date.issued2022-10-17
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionConference Name: CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, October 2022en_US
dc.descriptionDate of Conference: 17-21 October 2022en_US
dc.description.abstractData streams produce extensive data with high throughput from various domains and require copious amounts of computational resources and energy. Many data streams are generated as multi-labeled and classifying this data is computationally demanding. Some of the most well-known methods for Multi-Label Stream Classification are Problem Transformation schemes; however, previous work on this area does not satisfy the efficiency demands of multi-label data streams. In this study, we propose a novel Problem Transformation method for Multi-Label Stream Classification called Binary Transformation, which utilizes regression algorithms by transforming the labels into a continuous value. We compare our method against three of the leading problem transformation methods using eight datasets. Our results show that Binary Transformation achieves statistically similar effectiveness and provides a much higher level of efficiency.en_US
dc.identifier.doi10.1145/3511808.3557553en_US
dc.identifier.isbn9781450392365
dc.identifier.urihttp://hdl.handle.net/11693/111733
dc.language.isoEnglishen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.isversionofhttps://dx.doi.org/10.1145/3511808.3557553en_US
dc.subjectData streamen_US
dc.subjectClassificationen_US
dc.subjectMulti-labelen_US
dc.subjectProblem transformationen_US
dc.titleBinary transformation method for multi-label stream classificationen_US
dc.typeConference Paperen_US
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