Gülcan, Ege BerkayEcevit, Işın SuCan, Fazlı2023-02-252023-02-252022-10-179781450392365http://hdl.handle.net/11693/111733Conference Name: CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, October 2022Date of Conference: 17-21 October 2022Data 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.EnglishData streamClassificationMulti-labelProblem transformationConference Paper10.1145/3511808.3557553