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Autonomous anomaly detection

Gu, Xiaowei, Angelov, Plamen (2017) Autonomous anomaly detection. In: 2017 Evolving and Adaptive Intelligent Systems (EAIS). . pp. 1-8. IEEE ISBN 978-1-5090-6445-8. E-ISBN 978-1-5090-6444-1. (doi:10.1109/EAIS.2017.7954831) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90212)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1109/EAIS.2017.7954831

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.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/EAIS.2017.7954831
Uncontrolled keywords: Mathematical model; Euclidean distance; Data analysis; Chebyshev approximation; Statistical analysis; Partitioning algorithms; Silicon; autonomous anomaly detection; Empirical Data Analytics (EDA); nonparametric; data cloud
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 14 Sep 2021 14:21 UTC
Last Modified: 15 Sep 2021 14:51 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90212 (The current URI for this page, for reference purposes)
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