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• W2908457929 abstract "To perform anomaly detection for trajectory data, we study the Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD) approach, and propose an enhanced version called SHNN-CAD +. SHNN-CAD was introduced based on the theory of conformal prediction dealing with the problem of online detection. Unlike most related approaches requiring several not intuitive parameters, SHNN-CAD has the advantage of being parameter-light which enables the easy reproduction of experiments. We propose to adaptively determine the anomaly threshold during the online detection procedure instead of predefining it without any prior knowledge, which makes the algorithm more usable in practical applications. We present a modified Hausdorff distance measure that takes into account the direction difference and also reduces the computational complexity. In addition, the anomaly detection is more flexible and accurate via a re-do strategy. Extensive experiments on both real-world and synthetic data show that SHNN-CAD + outperforms SHNN-CAD with regard to accuracy and running time." @default.
• W2908457929 created "2019-01-11" @default.
• W2908457929 creator A5023978917 @default.
• W2908457929 creator A5082300315 @default.
• W2908457929 date "2018-12-27" @default.
• W2908457929 modified "2023-10-14" @default.
• W2908457929 title "SHNN-CAD+: An Improvement on SHNN-CAD for Adaptive Online Trajectory Anomaly Detection" @default.
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• W2908457929 doi "https://doi.org/10.3390/s19010084" @default.
• W2908457929 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6338912" @default.
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• W2908457929 hasPublicationYear "2018" @default.
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