  In contextual anomaly detection, an object is only considered anomalous
within a specific context. Most existing methods for CAD use a single context
based on a set of user-specified contextual features. However, identifying the
right context can be very challenging in practice, especially in datasets, with
a large number of attributes. Furthermore, in real-world systems, there might
be multiple anomalies that occur in different contexts and, therefore, require
a combination of several "useful" contexts to unveil them. In this work, we
leverage active learning and ensembles to effectively detect complex contextual
anomalies in situations where the true contextual and behavioral attributes are
unknown. We propose a novel approach, called WisCon (Wisdom of the Contexts),
that automatically creates contexts from the feature set. Our method constructs
an ensemble of multiple contexts, with varying importance scores, based on the
assumption that not all useful contexts are equally so. Experiments show that
WisCon significantly outperforms existing baselines in different categories
(i.e., active classifiers, unsupervised contextual and non-contextual anomaly
detectors, and supervised classifiers) on seven datasets. Furthermore, the
results support our initial hypothesis that there is no single perfect context
that successfully uncovers all kinds of contextual anomalies, and leveraging
the "wisdom" of multiple contexts is necessary.
