A comparison of one-class classifiers for novelty detection in forensic case data

Authors:
Frédéric Ratle;Mikhail Kanevski;Anne-Laure Terrettaz-Zufferey;Pierre Esseiva;Olivier Ribaux
Affiliations:
Institute of Geomatics and Risk Analysis, Faculty of Earth and Environmental Sciences, University of Lausanne, Switzerland;Institute of Geomatics and Risk Analysis, Faculty of Earth and Environmental Sciences, University of Lausanne, Switzerland;School of Criminal Sciences, Faculty of Law, University of Lausanne, Switzerland;School of Criminal Sciences, Faculty of Law, University of Lausanne, Switzerland;School of Criminal Sciences, Faculty of Law, University of Lausanne, Switzerland
Venue:
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Year:
2007

Citing 4
Cited 1

Novelty detection: a review—part 1: statistical approaches

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Novelty detection: a review—part 2: neural network based approaches

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Support Vector Data Description

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Learning manifolds in forensic data

ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II

One class random forests

Pattern Recognition

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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.