  The ever-growing speed at which data are generated nowadays, together with
the substantial cost of labeling processes cause Machine Learning models to
face scenarios in which data are partially labeled. The extreme case where such
a supervision is indefinitely unavailable is referred to as extreme
verification latency. On the other hand, in streaming setups data flows are
affected by exogenous factors that yield non-stationarities in the patterns
(concept drift), compelling models learned incrementally from the data streams
to adapt their modeled knowledge to the concepts within the stream. In this
work we address the casuistry in which these two conditions occur together, by
which adaptation mechanisms to accommodate drifts within the stream are
challenged by the lack of supervision, requiring further mechanisms to track
the evolution of concepts in the absence of verification. To this end we
propose a novel approach, AiGAS-dEVL (Adaptive Incremental neural GAS model for
drifting Streams under Extreme Verification Latency), which relies on growing
neural gas to characterize the distributions of all concepts detected within
the stream over time. Our approach exposes that the online analysis of the
behavior of these prototypical points over time facilitates the definition of
the evolution of concepts in the feature space, the detection of changes in
their behavior, and the design of adaptation policies to mitigate the effect of
such changes in the model. We assess the performance of AiGAS-dEVL over several
synthetic datasets, comparing it to that of state-of-the-art approaches
proposed in the recent past to tackle this stream learning setup. Our results
reveal that AiGAS-dEVL performs competitively with respect to the rest of
baselines, exhibiting a superior adaptability over several datasets in the
benchmark while ensuring a simple and interpretable instance-based adaptation
strategy.
