Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy

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

doi:10.1007/978-3-319-17091-6_23

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 proceeding › Conference contribution

Abstract

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 language	English
Title of host publication	Statistical Learning and Data Sciences
Subtitle of host publication	Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings
Editors	Alex Gammerman, Vladimir Vovk, Harris Papadopoulos
Publisher	Springer
Pages	281-290
Number of pages	10
ISBN (Electronic)	978-3-319-17091-6
ISBN (Print)	978-3-319-17090-9
DOIs	https://doi.org/10.1007/978-3-319-17091-6_23
Publication status	Published - 3 Apr 2015

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	9047

Access to Document

10.1007/978-3-319-17091-6_23

Cite this

Smith, J., Nouretdinov, I., Craddock, R., Offer, C., & Gammerman, A. (2015). Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy. In A. Gammerman, V. Vovk, & H. Papadopoulos (Eds.), Statistical Learning and Data Sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings (pp. 281-290). (Lecture Notes in Computer Science; Vol. 9047). Springer. https://doi.org/10.1007/978-3-319-17091-6_23

Smith, James ; Nouretdinov, Ilia ; Craddock, Rachel et al. / Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy. Statistical Learning and Data Sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. editor / Alex Gammerman ; Vladimir Vovk ; Harris Papadopoulos. Springer, 2015. pp. 281-290 (Lecture Notes in Computer Science).

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title = "Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy",

abstract = "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.",

author = "James Smith and Ilia Nouretdinov and Rachel Craddock and Charles Offer and Alex Gammerman",

year = "2015",

month = apr,

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doi = "10.1007/978-3-319-17091-6_23",

language = "English",

isbn = "978-3-319-17090-9",

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editor = "Alex Gammerman and Vladimir Vovk and Harris Papadopoulos",

booktitle = "Statistical Learning and Data Sciences",

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Smith, J, Nouretdinov, I, Craddock, R, Offer, C & Gammerman, A 2015, Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy. in A Gammerman, V Vovk & H Papadopoulos (eds), Statistical Learning and Data Sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. Lecture Notes in Computer Science, vol. 9047, Springer, pp. 281-290. https://doi.org/10.1007/978-3-319-17091-6_23

Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy. / Smith, James; Nouretdinov, Ilia; Craddock, Rachel et al.
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 proceeding › Conference contribution

TY - GEN

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AU - Smith, James

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AU - Offer, Charles

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AB - 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.

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Smith J, Nouretdinov I, Craddock R, Offer C, Gammerman A. Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy. In Gammerman A, Vovk V, Papadopoulos H, editors, Statistical Learning and Data Sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. Springer. 2015. p. 281-290. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-17091-6_23