Abstract
This paper describes conformal prediction techniques for detecting anomalous trajectories in the maritime domain. The data used in experiments were obtained from Automatic Identification System (AIS) broadcasts – a system for tracking vessel locations. A dimensionality reduction package is used and a kernel density estimation function as a non-conformity measure has been applied to detect anomalies. We propose average p-value as an efficiency criteria for conformal anomaly detection. A comparison with a k-nearest neighbours non-conformity measure is presented and the results are discussed.
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Smith, J., Nouretdinov, I., Craddock, R., Offer, C., Gammerman, A. (2014). Anomaly Detection of Trajectories with Kernel Density Estimation by Conformal Prediction. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H., Sioutas, S., Makris, C. (eds) Artificial Intelligence Applications and Innovations. AIAI 2014. IFIP Advances in Information and Communication Technology, vol 437. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44722-2_29
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DOI: https://doi.org/10.1007/978-3-662-44722-2_29
Publisher Name: Springer, Berlin, Heidelberg
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