Active learning with one-class classifiers involves users in the detection of outliers. The evaluation of one-class active learning typically relies on user feedback that is simulated, based on benchmark data. This is because validations with real users are elaborate. They require the de-sign and implementation of an interactive learning system. But without such a validation, it is unclear whether the value proposition of active learning does materialize when it comes to an actual detection of out-liers. User studies are necessary to find out when users can indeed provide feedback. In this article, we describe important characteristics and pre-requisites of one-class active learning for outlier detection, and how they influence the design of interactive systems. We propose a reference architecture of a one-class active learning system. We then describe design alternatives regarding such a system and discuss conceptual and technical challenges. We conclude with a roadmap towards validating one-class active learning with user studies
