$(1 + \varepsilon)$-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets
This addresses the problem of anomaly detection for applications with scarce or non-representative anomalous data, though it appears incremental as it builds on existing one-class and two-class methods.
The paper tackles anomaly detection in datasets with highly imbalanced or incomplete anomalous samples by proposing a novel method that trades off between one-class and two-class approaches, resulting in improved performance as evaluated on multiple datasets.
Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples. The method is evaluated using several data sets and compared to a set of conventional one-class and two-class approaches.