Positive and unlabeled learning for anomaly detection - Zhang, Jiaqi (2018)
Anomaly detection is of great interest to big data applications but still remains a challenging problem for machine learning-based methods. For unsupervised learning, the performance may not be satisfactory due to the lack of label information while for supervised learning, it is difficult to acquire labeled anomaly data for training which is usually rare and diversely distributed.