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Data"],"published-print":{"date-parts":[[2024,9,30]]},"abstract":"<jats:p>In the task of multi-label classification in data streams, instances arriving in real-time need to be associated with multiple labels simultaneously. Various methods based on the k Nearest Neighbors algorithm have been proposed to address this task. However, these methods face limitations when dealing with imbalanced data streams, a problem that has received limited attention in existing works. To approach this gap, this article introduces the Imbalance-Robust Multi-Label Self-Adjusting kNN (IRMLSAkNN), designed to tackle multi-label imbalanced data streams. IRMLSAkNN\u2019s strength relies on maintaining relevant instances with imbalance labels by using a discarding mechanism that considers the imbalance ratio per label. On the other hand, it evaluates subwindows with an imbalance-aware measure to discard older instances that are lacking performance. We conducted statistical experiments on 32 benchmark data streams, evaluating IRMLSAkNN against eight multi-label classification algorithms using common accuracy-aware and imbalance-aware measures. The obtained results demonstrate that IRMLSAkNN consistently outperforms these algorithms in terms of predictive capacity and time cost across various levels of imbalance.<\/jats:p>","DOI":"10.1145\/3663575","type":"journal-article","created":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T10:40:41Z","timestamp":1715424041000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Imbalance-Robust Multi-Label Self-Adjusting kNN"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9335-5247","authenticated-orcid":false,"given":"Victor Gomes De Oliveira Martins","family":"Nicola","sequence":"first","affiliation":[{"name":"University of S\u00e3o Paulo, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9120-8987","authenticated-orcid":false,"given":"Karina Valdivia","family":"Delgado","sequence":"additional","affiliation":[{"name":"University of S\u00e3o Paulo, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5507-2368","authenticated-orcid":false,"given":"Marcelo de Souza","family":"Lauretto","sequence":"additional","affiliation":[{"name":"University of S\u00e3o Paulo, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,7,26]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"4165","DOI":"10.1007\/s10994-023-06353-6","article-title":"A survey on learning from imbalanced data streams: Taxonomy, challenges, empirical study, and reproducible experimental framework","volume":"113","author":"Aguiar Gabriel","year":"2022","unstructured":"Gabriel Aguiar, Bartosz Krawczyk, and Alberto Cano. 2022. 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