Active Learning for Anomaly and Rare-Category Detection

We introduce a novel active-learning scenario in which a user wants to work with a learning algorithm to identify useful anomalies. These are distinguished from the traditional statistical definition of anomalies as outliers or merely ill-modeled points. Our distinction is that the usefulness ofanomalies is categorized subjectively by the user. We make two additional assumptions. First, there exist extremely few useful anomalies tobe hunted down within a massive dataset.