Data augmentation on-the-fly and active learning in data stream classification. (arXiv:2210.06873v1 [cs.LG])

There is an emerging need for predictive models to be trained on-the-fly,
since in numerous machine learning applications data are arriving in an online
fashion. A critical challenge encountered is that of limited availability of
ground truth information (e.g., labels in classification tasks) as new data are
observed one-by-one online, while another significant challenge is that of
class imbalance. This work introduces the novel Augmented Queues method, which
addresses the dual-problem by combining in a synergistic manner online active
learning, …

active learning arxiv augmentation classification data data stream