  The domain of radio astronomy is currently facing significant computational
challenges, foremost amongst which are those posed by the development of the
world's largest radio telescope, the Square Kilometre Array (SKA). Preliminary
specifications for this instrument suggest that the final design will
incorporate between 2000 and 3000 individual 15 metre receiving dishes, which
together can be expected to produce a data rate of many TB/s. Given such a high
data rate, it becomes crucial to consider how this information will be
processed and stored to maximise its scientific utility. In this paper, we
consider one possible data processing scenario for the SKA, for the purposes of
an all-sky pulsar survey. In particular we treat the selection of promising
signals from the SKA processing pipeline as a data stream classification
problem. We consider the feasibility of classifying signals that arrive via an
unlabelled and heavily class imbalanced data stream, using currently available
algorithms and frameworks. Our results indicate that existing stream learners
exhibit unacceptably low recall on real astronomical data when used in standard
configuration; however, good false positive performance and comparable accuracy
to static learners, suggests they have definite potential as an on-line
solution to this particular big data challenge.
