(1 + Epsilon)-Class Classification: An Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets
Abstract
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.
Cite
Text
Borisyak et al. "(1 + Epsilon)-Class Classification: An Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets." Journal of Machine Learning Research, 2020.Markdown
[Borisyak et al. "(1 + Epsilon)-Class Classification: An Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets." Journal of Machine Learning Research, 2020.](https://mlanthology.org/jmlr/2020/borisyak2020jmlr-epsilon/)BibTeX
@article{borisyak2020jmlr-epsilon,
title = {{(1 + Epsilon)-Class Classification: An Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets}},
author = {Borisyak, Maxim and Ryzhikov, Artem and Ustyuzhanin, Andrey and Derkach, Denis and Ratnikov, Fedor and Mineeva, Olga},
journal = {Journal of Machine Learning Research},
year = {2020},
pages = {1-22},
volume = {21},
url = {https://mlanthology.org/jmlr/2020/borisyak2020jmlr-epsilon/}
}