165 citations · 178 across the 3 of their papers we have counts for
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cs.LG2024★ 12 cited
Hoeffding adaptive trees for multi-label classification on data streams
Aurora Esteban, Alberto Cano, Amelia Zafra +1
Data stream learning is a very relevant paradigm because of the increasing real-world scenarios generating data at high velocities and in unbounded sequences. Stream learning aims…
cs.LG2022★ 165 cited
A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework
Gabriel Aguiar, Bartosz Krawczyk, Alberto Cano
Class imbalance poses new challenges when it comes to classifying data streams. Many algorithms recently proposed in the literature tackle this problem using a variety of data-leve…