@inproceedings{297c68b652244b33898383be75a631b9,
title = "Autonomous anomaly detection",
abstract = "In this paper, a new approach for autonomous anomaly detection is introduced within the Empirical Data Analytics (EDA) framework. This approach is fully data-driven and free from thresholds. Employing the nonparametric EDA estimators, the proposed approach can autonomously detect anomalies in an objective way based on the mutual distribution and ensemble properties of the data. The proposed approach firstly identifies the potential anomalies based on two EDA criteria, and then, partitions them into shape-free, non-parametric data clouds. Finally, it identifies the anomalies in regards to each data cloud (locally). Numerical examples based on synthetic and benchmark datasets demonstrate the validity and efficiency of the proposed approach.",
keywords = "Empirical Data Analytics (EDA), autonomous anomaly detection, data cloud, nonparametric",
author = "Xiaowei Gu and Angelov, {Plamen Parvanov}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.",
year = "2017",
month = jun,
day = "22",
doi = "10.1109/EAIS.2017.7954831",
language = "English",
isbn = "9781509064441",
series = "IEEE Conference on Evolving and Adaptive Intelligent Systems",
publisher = "IEEE Press",
editor = "Igor Skrjanc and Saso Blazic",
booktitle = "IEEE Conference on Evolving and Adaptive Intelligent Systems 2017",
address = "United States of America",
}