  Active learning methods increase classification quality by means of user
feedback. An important subcategory is active learning for outlier detection
with one-class classifiers. While various methods in this category exist,
selecting one for a given application scenario is difficult. This is because
existing methods rely on different assumptions, have different objectives, and
often are tailored to a specific use case. All this calls for a comprehensive
comparison, the topic of this article. This article starts with a
categorization of the various methods. We then propose ways to evaluate active
learning results. Next, we run extensive experiments to compare existing
methods, for a broad variety of scenarios. Based on our results, we formulate
guidelines on how to select active learning methods for outlier detection with
one-class classifiers.
