Abstract
Many of the existing approaches to anomaly detection are based upon supervised learning and heavily dependent on training datasets. However, anomalies rarely occur in most industrial systems. Hence it is challenging to retrieve a training dataset labeled with true anomalies. Therefore, this motivates us to investigate such scenarios where it is arduous to get labeled data for anomalies. This paper has proposed a clustering-based recursive anomaly detection algorithm; dynamic-Binary Tree Anomaly Identifier (d-BTAI). d-BTAI has been applied on industrial devices since anomalies in large industrial devices can incur massive losses. The algorithm has experimented on various publicly available industrial datasets such as Cloudwatch, Yahoo, and Backblaze. d-BTAI has attained a higher Area under the ROC curve (AUC) in comparison with Isolation Forest (iForest), One Class Support Vector Machine (OCSVM), and Elliptic Envelope. The higher Negative Predictive Value (NPV) and specificity value demonstrate the algorithm’s efficacy on multiple datasets.
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Sarkar, J., Sarkar, S., Saha, S., Das, S. (2021). d-BTAI: The Dynamic-Binary Tree Based Anomaly Identification Algorithm for Industrial Systems. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12799. Springer, Cham. https://doi.org/10.1007/978-3-030-79463-7_44
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