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 - 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 -