  The laborious process of labeling data often bottlenecks projects that aim to
leverage the power of supervised machine learning. Active Learning (AL) has
been established as a technique to ameliorate this condition through an
iterative framework that queries a human annotator for labels of instances with
the most uncertain class assignment. Via this mechanism, AL produces a binary
classifier trained on less labeled data but with little, if any, loss in
predictive performance. Despite its advantages, AL can have difficulty with
class-imbalanced datasets and results in an inefficient labeling process. To
address these drawbacks, we investigate our unsupervised instance selection
(UNISEL) technique followed by a Random Forest (RF) classifier on 10 outlier
detection datasets under low-label conditions. These results are compared to AL
performed on the same datasets. Further, we investigate the combination of
UNISEL and AL. Results indicate that UNISEL followed by an RF performs
comparably to AL with an RF and that the combination of UNISEL and AL
demonstrates superior performance. The practical implications of these findings
in terms of time savings and generalizability afforded by UNISEL are discussed.
