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.

中文翻译：

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一类分类及其在法医分析中的应用

对碎玻璃的分析对于重建犯罪行为的事件具有法医学意义。特别是，将嫌疑人发现的玻璃碎片（回收的案件）与犯罪现场收集的玻璃碎片（控制案件）进行比较，可以帮助警察正确识别犯罪者。法医问题可以归为一类分类问题。一类分类是最近出现的特殊分类任务，其中只有一个类是完全已知的（所谓的目标类），而关于其他类的信息则完全缺失。我们建议考虑基尼的经典变换概率作为典型性的度量，即观察值与一组众所周知的对象（控制案例）之间的相似性度量。提出的基于变换的一类分类器的目的是确定目标类周围的最佳边界，即在拒绝所有偏离该类的对象的同时，识别尽可能多的目标对象。