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One‐class classification with application to forensic analysis

Author

Listed:
  • Francesca Fortunato
  • Laura Anderlucci
  • Angela Montanari

Abstract

The analysis of broken glass is forensically important to reconstruct the events of a criminal act. In particular, the comparison between the glass fragments found on a suspect (recovered cases) and those collected at the crime scene (control cases) may help the police to identify the offender(s) correctly. The forensic issue can be framed as a one‐class classification problem. One‐class classification is a recently emerging and special classification task, where only one class is fully known (the so‐called target class), whereas information on the others is completely missing. We propose to consider Gini's classical transvariation probability as a measure of typicality, i.e. a measure of resemblance between an observation and a set of well‐known objects (the control cases). The aim of the proposed transvariation‐based one‐class classifier is to identify the best boundary around the target class, i.e. to recognize as many target objects as possible while rejecting all those deviating from this class.

Suggested Citation

  • Francesca Fortunato & Laura Anderlucci & Angela Montanari, 2020. "One‐class classification with application to forensic analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1227-1249, November.
    • Handle: RePEc:bla:jorssc:v:69:y:2020:i:5:p:1227-1249
    DOI: 10.1111/rssc.12438
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    References listed on IDEAS

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    1. Montanari, Angela & Lizzani, Laura, 2001. "A projection pursuit approach to variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 35(4), pages 463-473, February.
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