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