Anomaly Detection of Trajectories with Kernel Density Estimation by Conformal Prediction

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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Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos; Spyros Sioutas; Christos Makris. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-437, pp.271-280, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44722-2_29〉
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James Smith, Ilia Nouretdinov, Rachel Craddock, Charles Offer, Alexander Gammerman. Anomaly Detection of Trajectories with Kernel Density Estimation by Conformal Prediction. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos; Spyros Sioutas; Christos Makris. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-437, pp.271-280, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44722-2_29〉. 〈hal-01391054〉

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