Challenges of Profiling Offenders for Recidivism Risk-Reference-Cited by-同舟云学术

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Challenges of Profiling Offenders for Recidivism Risk

Published:2023-12-04 Issue: Volume:30 Page:235-244
ISSN:
Container-title:2023 IEEE International Conference on Data Mining Workshops (ICDMW)
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Author:

Jamil M. Luqman¹,Pais Sebastião¹,Pombo Nuno²,Cordeiro João³,Neves Pedro⁴

Affiliation:

1. University of Beira Interior and NOVA LINCS,Covilhã,Portugal

2. University of Beira Interior,Instituto de Telecomunicações,Covilhã,Portugal

3. University of Beira Interior and INESC TEC, Porto,Covilhã,Portugal

4. IPS - Innovative Prison Systems,Covilhã,Portugal

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10411504/10411508/10411645.pdf?arnumber=10411645

Reference20 articles.

1. A systematic review of criminal recidivism rates worldwide: 3-year update

2. Machine Learning and Criminal Justice: A Systematic Review of Advanced Methodology for Recidivism Risk Prediction

3. Which method predicts recidivism best?: a comparison of statistical, machine learning and data mining predictive models

4. An impact assessment of machine learning risk forecasts on parole board decisions and recidivism

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