Concept Drift Adaption for Online Anomaly Detection in Structural Health Monitoring. Tian, H., Khoa, N. L. D., Anaissi, A., Wang, Y., & Chen, F. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, of CIKM '19, pages 2813–2821, New York, NY, USA, November, 2019. Association for Computing Machinery. ![Concept Drift Adaption for Online Anomaly Detection in Structural Health Monitoring [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Despite its success for anomaly detection in the scenario where only data representing normal behavior are available, one-class support vector machine (OCSVM) still has challenge in dealing with non-stationary data stream, where the underlying distributions of data are time-varying. Existing OCSVM-based online learning methods incrementally update the model to address the challenge, however, they solely rely on the location relationship between a test sample and error support vectors. To better accommodate normal behavior evolution, online anomaly detection in non-stationary data stream is formulated as a concept drift adaptation problem in this paper. It is proposed that OCSVM-based incremental learning is only performed in the case of a normal drift. For an incoming sample, its relative relationship with three sets of vectors in OCSVM, namely margin support vectors, error support vectors, and reserve vectors is fully utilized to estimate whether a normal drift is emerging. Extensive experiments in the field of structural health monitoring have been conducted and the results have shown that the proposed simple approach outperforms the existing OCSVM-based online learning algorithms for anomaly detection.
@inproceedings{tian_concept_2019,
address = {New York, NY, USA},
series = {{CIKM} '19},
title = {Concept {Drift} {Adaption} for {Online} {Anomaly} {Detection} in {Structural} {Health} {Monitoring}},
isbn = {978-1-4503-6976-3},
url = {https://doi.org/10.1145/3357384.3357816},
doi = {10.1145/3357384.3357816},
abstract = {Despite its success for anomaly detection in the scenario where only data representing normal behavior are available, one-class support vector machine (OCSVM) still has challenge in dealing with non-stationary data stream, where the underlying distributions of data are time-varying. Existing OCSVM-based online learning methods incrementally update the model to address the challenge, however, they solely rely on the location relationship between a test sample and error support vectors. To better accommodate normal behavior evolution, online anomaly detection in non-stationary data stream is formulated as a concept drift adaptation problem in this paper. It is proposed that OCSVM-based incremental learning is only performed in the case of a normal drift. For an incoming sample, its relative relationship with three sets of vectors in OCSVM, namely margin support vectors, error support vectors, and reserve vectors is fully utilized to estimate whether a normal drift is emerging. Extensive experiments in the field of structural health monitoring have been conducted and the results have shown that the proposed simple approach outperforms the existing OCSVM-based online learning algorithms for anomaly detection.},
urldate = {2021-03-26},
booktitle = {Proceedings of the 28th {ACM} {International} {Conference} on {Information} and {Knowledge} {Management}},
publisher = {Association for Computing Machinery},
author = {Tian, Hongda and Khoa, Nguyen Lu Dang and Anaissi, Ali and Wang, Yang and Chen, Fang},
month = nov,
year = {2019},
keywords = {anomaly detection, concept drift, data stream, ecml, incremental/online learning, one-class support vector machine},
pages = {2813--2821},
}
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