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.