One-Class Active Learning for Outlier Detection with Multiple Subspaces

Active learning for outlier detection involves users in the process, by asking them for annotations of observations, in the form of class labels. The usual assumption is that users can provide such feedback, regardless of the nature and the presentation of the results. This is a simplification, which may not hold in practice. To overcome it, we propose SubSVDD, a semi-supervised classifier, that learns decision boundaries in low-dimensional projections of the data. SubSVDD de-constructs the outlier classification so that users can comprehend and interpret results more easily. For active learning, SubSVDD features a new update mechanism that adjusts decision boundaries based on user feedback. In particular, it considers that outliers may only occur in some of the low-dimensional projections. We conduct systematic experiments to show the effectiveness of our approach. In a comprehensive benchmark, SubSVDD outperforms alternative approaches on several data sets.