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A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework. (arXiv:2204.03719v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
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 algorithms in a collection of
diverse and challenging imbalanced data stream …
arxiv challenges data experimental framework learning study survey taxonomy