Multi-expert estimations of burglars’ risk exposure and level of pre-crime preparation based on crime scene data

Martin Boldt
Department of Computer Science and Engineering, Blekinge Institute of Technology, Sweden

Veselka Boeva
Department of Computer Science and Engineering, Blekinge Institute of Technology, Sweden

Anton Borg
Department of Computer Science and Engineering, Blekinge Institute of Technology, Sweden

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Ingår i: 30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden

Linköping Electronic Conference Proceedings 137:4, s. 39-44

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Publicerad: 2017-05-12

ISBN: 978-91-7685-496-9

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


Law enforcement agencies strive to link crimes perpetrated by the same offenders into crime series in order to improve investigation efficiency. Such crime linkage can be done using both physical traces (e.g., DNA or fingerprints) or “soft evidence” in the form of offenders’ modus operandi (MO), i.e. their behaviors during crimes. However, physical traces are only present for a fraction of crimes, unlike behavioral evidence. This position paper presents a method for aggregating multiple criminal profilers’ ratings of offenders’ behavioral characteristics based on feature-rich crime scene descriptions. The method calculates consensus ratings from individual experts’ ratings, which then are used as a basis for classification algorithms. The classification algorithms can automatically generalize offenders’ behavioral characteristics from cues in the crime scene data. Models trained on the consensus rating are evaluated against models trained on individual profiler’s ratings. Thus, whether the consensus model shows improved performance over individual models.


Multi-expert decision making, Classification, Crime linkage, Offender profiling


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