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Applications of Autonomous Anomaly Detection

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)

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Applications of Autonomous Anomaly Detection. / Angelov, P.P.; Gu, X.

Empirical Approach to Machine Learning. ed. / Plamen Angelov; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. p. 249-259 (Studies in Computational Intelligence; Vol. 800).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)

Harvard

Angelov, PP & Gu, X 2019, Applications of Autonomous Anomaly Detection. in P Angelov & X Gu (eds), Empirical Approach to Machine Learning. vol. 800, Studies in Computational Intelligence, vol. 800, Springer-Verlag, pp. 249-259. https://doi.org/10.1007/978-3-030-02384-3_10

APA

Angelov, P. P., & Gu, X. (2019). Applications of Autonomous Anomaly Detection. In P. Angelov, & X. Gu (Eds.), Empirical Approach to Machine Learning (Vol. 800, pp. 249-259). (Studies in Computational Intelligence; Vol. 800). Springer-Verlag. https://doi.org/10.1007/978-3-030-02384-3_10

Vancouver

Angelov PP, Gu X. Applications of Autonomous Anomaly Detection. In Angelov P, Gu X, editors, Empirical Approach to Machine Learning. Vol. 800. Springer-Verlag. 2019. p. 249-259. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-030-02384-3_10

Author

Angelov, P.P. ; Gu, X. / Applications of Autonomous Anomaly Detection. Empirical Approach to Machine Learning. editor / Plamen Angelov ; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. pp. 249-259 (Studies in Computational Intelligence).

Bibtex

@inbook{b9c99aab6f48493d8ce54694e7496f64,
title = "Applications of Autonomous Anomaly Detection",
abstract = "In this chapter, the algorithm summary of the proposed autonomous anomaly detection (AAD) algorithm described in Chap. 6 is provided. Numerical examples based on both the synthetic and benchmark datasets are presented for evaluating the performance of the AAD algorithm. Well-known traditional anomaly detection approaches are used for a further comparison. It was demonstrated through the numerical experiments that the AAD algorithm is able to provide a more objective, accurate way for anomaly detection, and its performance is not influenced by the structure of the data and is equally effective in detecting collective anomalies as well as individual anomalies. The pseudo-code of the main procedure of the AAD algorithm and the MATLAB implementation can be found in Appendices B.1 and C.1, respectively. {\textcopyright} 2019, Springer Nature Switzerland AG.",
author = "P.P. Angelov and X. Gu",
year = "2019",
doi = "10.1007/978-3-030-02384-3_10",
language = "English",
isbn = "9783030023836",
volume = "800",
series = "Studies in Computational Intelligence",
publisher = "Springer-Verlag",
pages = "249--259",
editor = "Plamen Angelov and Gu, {Xiaowei }",
booktitle = "Empirical Approach to Machine Learning",

}

RIS

TY - CHAP

T1 - Applications of Autonomous Anomaly Detection

AU - Angelov, P.P.

AU - Gu, X.

PY - 2019

Y1 - 2019

N2 - In this chapter, the algorithm summary of the proposed autonomous anomaly detection (AAD) algorithm described in Chap. 6 is provided. Numerical examples based on both the synthetic and benchmark datasets are presented for evaluating the performance of the AAD algorithm. Well-known traditional anomaly detection approaches are used for a further comparison. It was demonstrated through the numerical experiments that the AAD algorithm is able to provide a more objective, accurate way for anomaly detection, and its performance is not influenced by the structure of the data and is equally effective in detecting collective anomalies as well as individual anomalies. The pseudo-code of the main procedure of the AAD algorithm and the MATLAB implementation can be found in Appendices B.1 and C.1, respectively. © 2019, Springer Nature Switzerland AG.

AB - In this chapter, the algorithm summary of the proposed autonomous anomaly detection (AAD) algorithm described in Chap. 6 is provided. Numerical examples based on both the synthetic and benchmark datasets are presented for evaluating the performance of the AAD algorithm. Well-known traditional anomaly detection approaches are used for a further comparison. It was demonstrated through the numerical experiments that the AAD algorithm is able to provide a more objective, accurate way for anomaly detection, and its performance is not influenced by the structure of the data and is equally effective in detecting collective anomalies as well as individual anomalies. The pseudo-code of the main procedure of the AAD algorithm and the MATLAB implementation can be found in Appendices B.1 and C.1, respectively. © 2019, Springer Nature Switzerland AG.

U2 - 10.1007/978-3-030-02384-3_10

DO - 10.1007/978-3-030-02384-3_10

M3 - Chapter (peer-reviewed)

SN - 9783030023836

VL - 800

T3 - Studies in Computational Intelligence

SP - 249

EP - 259

BT - Empirical Approach to Machine Learning

A2 - Angelov, Plamen

A2 - Gu, Xiaowei

PB - Springer-Verlag

ER -