  Learning from non-stationary data streams is a research direction that gains
increasing interest as more data in form of streams becomes available, for
example from social media, smartphones, or industrial process monitoring. Most
approaches assume that the ground truth of the samples becomes available
(possibly with some delay) and perform supervised online learning in the
test-then-train scheme. While this assumption might be valid in some scenarios,
it does not apply to all settings. In this work, we focus on scarcely labeled
data streams and explore the potential of self-labeling in gradually drifting
data streams. We formalize this setup and propose a novel online $k$-nn
classifier that combines self-labeling and demand-based active learning.
