Binary transformation method for multi-label stream classification
buir.contributor.author | Gülcan, Ege Berkay | |
buir.contributor.author | Ecevit, Işın Su | |
buir.contributor.author | Can, Fazlı | |
buir.contributor.orcid | Gülcan, Ege Berkay|0000-0003-1237-0829 | |
buir.contributor.orcid | Ecevit, Işın Su|0000-0002-1666-785X | |
buir.contributor.orcid | Can, Fazlı|0000-0003-0016-4278 | |
dc.citation.epage | 3972 | en_US |
dc.citation.spage | 3968 | en_US |
dc.contributor.author | Gülcan, Ege Berkay | |
dc.contributor.author | Ecevit, Işın Su | |
dc.contributor.author | Can, Fazlı | |
dc.coverage.spatial | Atlanta, United States | en_US |
dc.date.accessioned | 2023-02-25T11:18:56Z | |
dc.date.available | 2023-02-25T11:18:56Z | |
dc.date.issued | 2022-10-17 | |
dc.department | Department of Computer Engineering | en_US |
dc.description | Conference Name: CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, October 2022 | en_US |
dc.description | Date of Conference: 17-21 October 2022 | en_US |
dc.description.abstract | Data 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.description.provenance | Submitted by Cem Çağatay Akgün (cem.akgun@bilkent.edu.tr) on 2023-02-25T11:18:56Z No. of bitstreams: 1 Binary_Transformation_Method_for_Multi-Label_Stream_Classification.pdf: 1349641 bytes, checksum: 5a6a998a9060eada1aa2ed08a0076d1a (MD5) | en |
dc.description.provenance | Made available in DSpace on 2023-02-25T11:18:56Z (GMT). No. of bitstreams: 1 Binary_Transformation_Method_for_Multi-Label_Stream_Classification.pdf: 1349641 bytes, checksum: 5a6a998a9060eada1aa2ed08a0076d1a (MD5) Previous issue date: 2022-10-17 | en |
dc.identifier.doi | 10.1145/3511808.3557553 | en_US |
dc.identifier.isbn | 9781450392365 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/111733 | en_US |
dc.language.iso | English | en_US |
dc.publisher | Association for Computing Machinery | en_US |
dc.relation.isversionof | https://dx.doi.org/10.1145/3511808.3557553 | en_US |
dc.subject | Data stream | en_US |
dc.subject | Classification | en_US |
dc.subject | Multi-label | en_US |
dc.subject | Problem transformation | en_US |
dc.title | Binary transformation method for multi-label stream classification | en_US |
dc.type | Conference Paper | en_US |
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