  In many applications, an anomaly detection system presents the most anomalous
data instance to a human analyst, who then must determine whether the instance
is truly of interest (e.g. a threat in a security setting). Unfortunately, most
anomaly detectors provide no explanation about why an instance was considered
anomalous, leaving the analyst with no guidance about where to begin the
investigation. To address this issue, we study the problems of computing and
evaluating sequential feature explanations (SFEs) for anomaly detectors. An SFE
of an anomaly is a sequence of features, which are presented to the analyst one
at a time (in order) until the information contained in the highlighted
features is enough for the analyst to make a confident judgement about the
anomaly. Since analyst effort is related to the amount of information that they
consider in an investigation, an explanation's quality is related to the number
of features that must be revealed to attain confidence. One of our main
contributions is to present a novel framework for large scale quantitative
evaluations of SFEs, where the quality measure is based on analyst effort. To
do this we construct anomaly detection benchmarks from real data sets along
with artificial experts that can be simulated for evaluation. Our second
contribution is to evaluate several novel explanation approaches within the
framework and on traditional anomaly detection benchmarks, offering several
insights into the approaches.
