One‐class classification with application to forensic analysis. Issue 5 (26th August 2020)

• Record Type: Journal Article
• Authors: Fortunato, Francesca; Anderlucci, Laura; Montanari, Angela
• Is Part Of: Journal of the Royal Statistical Society. Volume 69:Issue 5(2020)
• Page Start: 1227
• Page End: 1249
• Publication Date: 2020-08-26
• Subjects: Data depth measure -- One‐class classification -- Transvariation probability; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12438
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: 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.