A Machine Learning Generalization of LSI-OR



M.Sc. Thesis Defence: Rahim Oraji

M.Sc. Thesis Defence: Rahim Oraji

The Level of Service Inventory-Ontario Revision (LSI-OR) is used as a risk/need assessment tool to classify, manage, and treat the offender population so that they receive supportive services consistent with their custodial needs. This thesis adopts a machine learning approach employing the Naïve Bayes technique as an alternative to the LSI-OR.

The study was conducted on a group of (72725) offenders with different races and includes males (82.62%) and females (17.38%). Participants were monitored for two years to collect recidivism information. A basic analysis of the dataset revealed that 1) 83.18% of population used a unique pattern to answer 43 LSI-OR items, 2) the total LSI-OR scores in the entire population and also in male and female population followed two beta distribution functions, one for each recidivism class, and 3) the recidivism rate was approximated by a normal distribution function.

The dataset contained many features that are never used by the LSI-OR assessment for instance, the offence severity. A model was built at each index of offence severity based on LSI-OR scores and 43 LSI-OR items as input features. The results of running the experiment indicated that considering 43 LSI-OR items gives a more stable prediction than the LSI-OR scores.

Monday, May 16, 2016 @ 9:30 am

Westgrid, Room 2D71, Agriculture Building

M. Sc. Examining Committee:     

  • Vijay Mago, Lakehead University, External
  • Ray Spiteri, Supervisor
  • Mark Eramian
  • Kevin Stanley