Title: Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability,
Abstract: Anomaly detection (AD) task corresponds to identifying the true anomalies
from a given set of data instances. AD algorithms score the data instances and
produce a ranked list of candidate anomalies, which are then analyzed by a
human to discover the true anomalies. However, this process can be laborious
for the human analyst when the number of false-positives is very high.
Therefore, in many real-world AD applications including computer security and
fraud prevention, the anomaly detector must be configurable by the human
analyst to minimize the effort on false positives.
In this paper, we study the problem of active learning to automatically tune
ensemble of anomaly detectors to maximize the number of true anomalies
discovered. We make four main contributions towards this goal. First, we
present an important insight that explains the practical successes of AD
ensembles and how ensembles are naturally suited for active learning. Second,
we present several algorithms for active learning with tree-based AD ensembles.
These algorithms help us to improve the diversity of discovered anomalies,
generate rule sets for improved interpretability of anomalous instances, and
adapt to streaming data settings in a principled manner. Third, we present a
novel algorithm called GLocalized Anomaly Detection (GLAD) for active learning
with generic AD ensembles. GLAD allows end-users to retain the use of simple
and understandable global anomaly detectors by automatically learning their
local relevance to specific data instances using label feedback. Fourth, we
present extensive experiments to evaluate our insights and algorithms. Our
results show that in addition to discovering significantly more anomalies than
state-of-the-art unsupervised baselines, our active learning algorithms under
the streaming-data setup are competitive with the batch setup.