On the detection of rare events

Rare event detection is the task of detecting and identifying low-frequency but high-impact events. Such events include, in particular, certain types of rare diseases, complex cyberattacks, industrial equipment failures, and so on. Predicting and classifying them is challenging due to the limited number of observations and significant differences in frequency characteristics, which can vary greatly across different domains. A wide range of rare event detection and classification methods have been developed, including modern deep learning approaches capable of achieving high accuracy even with insufficient labeled training data. In this paper, we propose a new approach to improving the quality of such models.