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Applications of Autonomous Anomaly Detection

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

Publication date2019
Host publicationEmpirical Approach to Machine Learning
EditorsPlamen Angelov, Xiaowei Gu
Number of pages11
ISBN (print)9783030023836
<mark>Original language</mark>English

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


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.