Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Multi-expert estimations of burglars' risk exposure and level of pre-crime preparation using coded crime scene data: Work in progress
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.ORCID-id: 0000-0002-9316-4842
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.ORCID-id: 0000-0003-3128-191x
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.ORCID-id: 0000-0002-8929-7220
2018 (engelsk)Inngår i: Proceedings - 2018 European Intelligence and Security Informatics Conference, EISIC 2018 / [ed] Brynielsson, J, Institute of Electrical and Electronics Engineers Inc. , 2018, s. 77-80Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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 work-in-progress 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. © 2018 IEEE.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers Inc. , 2018. s. 77-80
Emneord [en]
classification, crime linkage, Multi-expert decision making, offender profiling, Classification (of information), Risk management, Risk perception, Behavioral characteristics, Classification algorithm, Consensus models, Individual models, Law-enforcement agencies, Work in progress, Crime
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-18621DOI: 10.1109/EISIC.2018.00021ISI: 000483031300012Scopus ID: 2-s2.0-85069498311ISBN: 9781538694008 (tryckt)OAI: oai:DiVA.org:bth-18621DiVA, id: diva2:1349904
Konferanse
8th European Intelligence and Security Informatics Conference, EISIC, Karlskrona, 24 October 2018 through 25 October 2018
Tilgjengelig fra: 2019-09-10 Laget: 2019-09-10 Sist oppdatert: 2019-09-19bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstScopus

Personposter BETA

Boldt, MartinBoeva, VeselkaBorg, Anton

Søk i DiVA

Av forfatter/redaktør
Boldt, MartinBoeva, VeselkaBorg, Anton
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric

doi
isbn
urn-nbn
Totalt: 58 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf