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Autonomous anomaly detection

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published

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Autonomous anomaly detection. / Gu, Xiaowei; Angelov, Plamen Parvanov.

IEEE Conference on Evolving and Adaptive Intelligent Systems. 2017. p. 1-8.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Gu, X & Angelov, PP 2017, Autonomous anomaly detection. in IEEE Conference on Evolving and Adaptive Intelligent Systems. pp. 1-8, IEEE Conference on Evolving and Adaptive Intelligent Systems , 31/05/17.

APA

Gu, X., & Angelov, P. P. (2017). Autonomous anomaly detection. In IEEE Conference on Evolving and Adaptive Intelligent Systems (pp. 1-8)

Vancouver

Gu X, Angelov PP. Autonomous anomaly detection. In IEEE Conference on Evolving and Adaptive Intelligent Systems. 2017. p. 1-8

Author

Gu, Xiaowei ; Angelov, Plamen Parvanov. / Autonomous anomaly detection. IEEE Conference on Evolving and Adaptive Intelligent Systems. 2017. pp. 1-8

Bibtex

@inproceedings{358ecb1ca5d84bad929da74b0e43a871,
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 criterions, 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.",
author = "Xiaowei Gu and Angelov, {Plamen Parvanov}",
year = "2017",
month = may,
day = "31",
language = "English",
pages = "1--8",
booktitle = "IEEE Conference on Evolving and Adaptive Intelligent Systems",
note = "IEEE Conference on Evolving and Adaptive Intelligent Systems ; Conference date: 31-05-2017 Through 02-06-2017",

}

RIS

TY - GEN

T1 - Autonomous anomaly detection

AU - Gu, Xiaowei

AU - Angelov, Plamen Parvanov

PY - 2017/5/31

Y1 - 2017/5/31

N2 - 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 criterions, 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.

AB - 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 criterions, 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.

M3 - Conference contribution/Paper

SP - 1

EP - 8

BT - IEEE Conference on Evolving and Adaptive Intelligent Systems

T2 - IEEE Conference on Evolving and Adaptive Intelligent Systems

Y2 - 31 May 2017 through 2 June 2017

ER -