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.
This work is supported by the Swiss National Science Foundation (grant no.105211-107862).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Esseiva, P., Dujourdy, L., Anglada, F., Taroni, F., Margot, P.: A methodology for illicit heroin seizures comparison in a drug intelligence perspective using large databases. Forensic Science International 132, 139–152 (2003)
Esseiva, P., Anglada, F., Dujourdy, L., Taroni, F., Margot, P., Du Pasquier, E., Dawson, M., Roux, C., Doble, P.: Chemical profiling and classification of illicit heroin by principal component analysis. Calculation of inter sample correlation and artificial neural networks, Talanta 67, 360–367 (2005)
Ratle, F., Terrettaz-Zufferey, A.L., Kanevski, M., Esseiva, P., Ribaux, O.: Pattern analysis in illicit heroin seizures: a novel application of machine learning algorithms. In: Proc. of the 14th European Symposium on Artificial Neural Networks, d-side publi. (2006)
Ratle, F., Terrettaz-Zufferey, A.L., Kanevski, M., Esseiva, P., Ribaux, O.: Learning manifolds in forensic data. In: Proc. of the 16th Int. Conf. on Artificial Neural Networks, Springer, Heidelberg (2006)
Tong, C., Svetnik, V.: Novelty detection in mass spectral data using a support vector machine method. In: Proc. of Interface 2002 (2002)
Markou, M., Singh, S.: Novelty detection: a review - part 1: statistical approaches. Signal Processing 83, 2481–2497 (2003)
Markou, M., Singh, S.: Novelty detection: a review - part 2: neural network based approaches. Signal Processing 83, 2499–2521 (2003)
Marsland, S.: Novelty detection in learning systems. Neural Computing Surveys 3, 157–195 (2003)
Tax, D.M.J.: One-class classification, Ph.D. thesis, University of Amsterdam (2001)
Tax, D.M.J., Duin, R.P.W.: Support Vector Data Description. Machine Learning 54, 45–66 (2004)
Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support vector method for novelty detection. Advances in Neural Information Processing Systems 12 (2000)
Tax, D.M.J.: Combining one-class classifiers. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, Springer, Heidelberg (2001)
Guéniat, O., Esseiva, P.: Le Profilage de l’Héroïne et de la Cocaïne, Presses polytechniques et universitaires romandes, Lausanne (2005)
Tax, D.M.J.: DDtools, the Data Description Toolbox for Matlab, version 1.5.7 (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ratle, F., Kanevski, M., Terrettaz-Zufferey, AL., Esseiva, P., Ribaux, O. (2007). A Comparison of One-Class Classifiers for Novelty Detection in Forensic Case Data. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-77226-2_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77225-5
Online ISBN: 978-3-540-77226-2
eBook Packages: Computer ScienceComputer Science (R0)