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Anomaly Detection—Empirical Approach

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review

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Anomaly Detection—Empirical Approach. / Angelov, P.P.; Gu, X.
Empirical Approach to Machine Learning. ed. / Plamen P. Angelov; Xiaowei Gu. Cham: Springer, 2019. p. 157-173 (Studies in Computational Intelligence; Vol. 800).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review

Harvard

Angelov, PP & Gu, X 2019, Anomaly Detection—Empirical Approach. in PP Angelov & X Gu (eds), Empirical Approach to Machine Learning. Studies in Computational Intelligence, vol. 800, Springer, Cham, pp. 157-173. https://doi.org/10.1007/978-3-030-02384-3_6

APA

Angelov, P. P., & Gu, X. (2019). Anomaly Detection—Empirical Approach. In P. P. Angelov, & X. Gu (Eds.), Empirical Approach to Machine Learning (pp. 157-173). (Studies in Computational Intelligence; Vol. 800). Springer. https://doi.org/10.1007/978-3-030-02384-3_6

Vancouver

Angelov PP, Gu X. Anomaly Detection—Empirical Approach. In Angelov PP, Gu X, editors, Empirical Approach to Machine Learning. Cham: Springer. 2019. p. 157-173. (Studies in Computational Intelligence). Epub 2018 Oct 18. doi: 10.1007/978-3-030-02384-3_6

Author

Angelov, P.P. ; Gu, X. / Anomaly Detection—Empirical Approach. Empirical Approach to Machine Learning. editor / Plamen P. Angelov ; Xiaowei Gu. Cham : Springer, 2019. pp. 157-173 (Studies in Computational Intelligence).

Bibtex

@inbook{d38ade225a924f85aece825332b87855,
title = "Anomaly Detection—Empirical Approach",
abstract = "In this chapter, the empirical approach to the problem of anomaly detection is presented, which is free from the pre-defined model and user-and problem-specific parameters and is data driven. The well-known Chebyshev inequality has been simplified by using the standardized eccentricity. An autonomous anomaly detection method is proposed, which is composed of two stages. In the first stage, all the potential global anomalies are selected out based on the data density and/or on the typicality, and in the second stage, the local anomalies are identified based on the data clouds formed from the potential global anomalies. In addition, a fully autonomous approach for the problem of fault detection has been outlined, which can also be extended to a fully autonomous fault detection and isolation approach. {\textcopyright} 2019, Springer Nature Switzerland AG.",
author = "P.P. Angelov and X. Gu",
year = "2019",
doi = "10.1007/978-3-030-02384-3_6",
language = "English",
isbn = "9783030023836",
series = "Studies in Computational Intelligence",
publisher = "Springer",
pages = "157--173",
editor = "Angelov, {Plamen P.} and Xiaowei Gu",
booktitle = "Empirical Approach to Machine Learning",

}

RIS

TY - CHAP

T1 - Anomaly Detection—Empirical Approach

AU - Angelov, P.P.

AU - Gu, X.

PY - 2019

Y1 - 2019

N2 - In this chapter, the empirical approach to the problem of anomaly detection is presented, which is free from the pre-defined model and user-and problem-specific parameters and is data driven. The well-known Chebyshev inequality has been simplified by using the standardized eccentricity. An autonomous anomaly detection method is proposed, which is composed of two stages. In the first stage, all the potential global anomalies are selected out based on the data density and/or on the typicality, and in the second stage, the local anomalies are identified based on the data clouds formed from the potential global anomalies. In addition, a fully autonomous approach for the problem of fault detection has been outlined, which can also be extended to a fully autonomous fault detection and isolation approach. © 2019, Springer Nature Switzerland AG.

AB - In this chapter, the empirical approach to the problem of anomaly detection is presented, which is free from the pre-defined model and user-and problem-specific parameters and is data driven. The well-known Chebyshev inequality has been simplified by using the standardized eccentricity. An autonomous anomaly detection method is proposed, which is composed of two stages. In the first stage, all the potential global anomalies are selected out based on the data density and/or on the typicality, and in the second stage, the local anomalies are identified based on the data clouds formed from the potential global anomalies. In addition, a fully autonomous approach for the problem of fault detection has been outlined, which can also be extended to a fully autonomous fault detection and isolation approach. © 2019, Springer Nature Switzerland AG.

U2 - 10.1007/978-3-030-02384-3_6

DO - 10.1007/978-3-030-02384-3_6

M3 - Chapter (peer-reviewed)

SN - 9783030023836

T3 - Studies in Computational Intelligence

SP - 157

EP - 173

BT - Empirical Approach to Machine Learning

A2 - Angelov, Plamen P.

A2 - Gu, Xiaowei

PB - Springer

CY - Cham

ER -