Stream-based Active Learning with Verification Latency in Non-stationary Environments. (arXiv:2204.06822v2 [cs.LG] UPDATED)

Data stream classification is an important problem in the field of machine
learning. Due to the non-stationary nature of the data where the underlying
distribution changes over time (concept drift), the model needs to continuously
adapt to new data statistics. Stream-based Active Learning (AL) approaches
address this problem by interactively querying a human expert to provide new
data labels for the most recent samples, within a limited budget. Existing AL
strategies assume that labels are immediately available, while in a …

active learning arxiv environments verification