Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy

James Smith, Ilia Nouretdinov, Rachel Craddock, Charles Offer, Alex Gammerman

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

Cite this