Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy. / Smith, James; Nouretdinov, Ilia; Craddock, Rachel; Offer, Charles; Gammerman, Alex.

Statistical Learning and Data Sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. ed. / Alex Gammerman; Vladimir Vovk; Harris Papadopoulos. Springer, 2015. p. 281-290 (Lecture Notes in Computer Science; Vol. 9047).

Research output: Chapter in Book/Report/Conference proceedingConference contribution



The paper investigates the problem of anomaly detection in the maritime trajectory surveillance domain. Conformal predictors in this paper are used as a basis for anomaly detection. A multi-class hierarchy framework is presented for different class representations. Experiments are conducted with data taken from shipping vessel trajectories using data obtained through AIS (Automatic Identification System) broadcasts and the results are discussed.
Original languageEnglish
Title of host publicationStatistical Learning and Data Sciences
Subtitle of host publicationThird International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings
EditorsAlex Gammerman, Vladimir Vovk, Harris Papadopoulos
Number of pages10
ISBN (Electronic)978-3-319-17091-6
ISBN (Print)978-3-319-17090-9
Publication statusPublished - 3 Apr 2015

Publication series

NameLecture Notes in Computer Science
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 24468132