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A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework
[article]
2022
arXiv
pre-print
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-level, algorithm-level, and ensemble approaches. However, there is a lack of standardized and agreed-upon procedures on how to evaluate these algorithms. This work presents a taxonomy of algorithms for imbalanced data streams and proposes a standardized, exhaustive, and informative experimental testbed to evaluate
arXiv:2204.03719v1
fatcat:dulhr3cedrh6vd6m5m4qovffri