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.
@article{arxiv.1906.06096,
title = {$(1 + \varepsilon)$-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets},
author = {Maxim Borisyak and Artem Ryzhikov and Andrey Ustyuzhanin and Denis Derkach and Fedor Ratnikov and Olga Mineeva},
journal= {arXiv preprint arXiv:1906.06096},
year = {2020}
}