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A Comparison of One-Class Classifiers for Novelty Detection in Forensic Case Data

  • Frédéric Ratle
  • Mikhail Kanevski
  • Anne-Laure Terrettaz-Zufferey
  • Pierre Esseiva
  • Olivier Ribaux
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)

Abstract

This paper investigates the application of novelty detection techniques to the problem of drug profiling in forensic science. Numerous one-class classifiers are tried out, from the simple k-means to the more elaborate Support Vector Data Description algorithm. The target application is the classification of illicit drugs samples as part of an existing trafficking network or as a new cluster. A unique chemical database of heroin and cocaine seizures is available and allows assessing the methods. Evaluation is done using the area under the ROC curve of the classifiers. Gaussian mixture models and the SVDD method are trained both with and without outlier examples, and it is found that providing outliers during training improves in some cases the classification performance. Finally, combination schemes of classifiers are also tried out. Results highlight methods that may guide the profiling methodology used in forensic analysis.

Keywords

Receiver Operating Characteristic Curve Gaussian Mixture Model Target Data Novelty Detection Support Vector Data Description 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Frédéric Ratle
    • 1
  • Mikhail Kanevski
    • 1
  • Anne-Laure Terrettaz-Zufferey
    • 2
  • Pierre Esseiva
    • 2
  • Olivier Ribaux
    • 2
  1. 1.Institute of Geomatics and Risk Analysis, Faculty of Earth and Environmental Sciences, University of Lausanne, CH-1015Switzerland
  2. 2.School of Criminal Sciences, Faculty of Law, University of Lausanne, CH-1015Switzerland

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