Anomaly detection methods aim to identify examples that do not follow the expected behavior. For various reasons, anomaly detection is typically tackled by using unsupervised approaches that assign real-valued anomaly scores based on various …
Anomaly detection attempts at finding examples that deviate from the expected behaviour. Usually, anomaly detection is tackled from an unsupervised perspective because anomalous labels are rare and difficult to acquire. However, the lack of labels …
Estimating the proportion of positive examples (i.e., the class prior) from positive and unlabeled (PU) data is an important task that facilitates learning a classifier from such data. In this paper, we explore how to tackle this problem when the …