Learning to Rank the Distinctiveness of Behaviour in Serial Offending

Authors: Law, M., Sautory, T., Mitchener, L., Davies, K., Tonkin, M., Woodhams, J. and Alrajeh, D.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 13416 LNAI

Pages: 484-497

eISSN: 1611-3349

ISBN: 9783031157066

ISSN: 0302-9743

DOI: 10.1007/978-3-031-15707-3_37

Abstract:

Comparative Case Analysis is an analytical process used to detect serial offending. It focuses on identifying distinctive behaviour that an offender displays consistently when committing their crimes. In practice, crime analysts consider the context in which each behaviour occurs to determine its distinctiveness, which subsequently impacts on their determination of whether crimes are committed by the same person or not. Existing algorithms do not currently consider context in this way when generating linkage predictions. This paper presents the first learning-based approach aimed at identifying contexts within which behaviour may be considered more distinctive. We show how this problem can be modelled as that of learning preferences (in answer set programming) from examples of ordered pairs of contexts in which a behaviour was observed. In this setting, a context is preferred to another context if the behaviour is rarer in the first context. We make novel use of odds ratios to determine which examples are used for learning. Our approach has been applied to a real dataset of serious sexual offences provided by the UK National Crime Agency. The approach provides (i) a systematic methodology for selecting examples from which to learn preferences; (ii) novel insights for practitioners into the contexts under which an exhibited behaviour is more rare.

Source: Scopus