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.
Type de document :
Communication dans un congrès
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〉
https://hal.inria.fr/hal-01391054
Contributeur : Hal Ifip
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Soumis le : mercredi 2 novembre 2016 - 17:18:32
Dernière modification le : vendredi 1 décembre 2017 - 01:16:37
Document(s) archivé(s) le : vendredi 3 février 2017 - 15:50:26
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〉
