Development of Risk Assessment Framework for First Time Offenders Using Ensemble Learning

Abstract

Recidivism is generally considered as a deficiency disease in which offenders recommend a crime or repeat an offence. Empirically committing the first crime at a very young age leads to a much higher rebound rate and the continuation of similar offensive behavior. Accordingly, prioritization must be given for the early assessment of recidivism behavior in first-time offender by law enforcing agencies. Different prison studies suggest that recidivism can be curtailed by early behavioral risk assessment in first-time offenders. Ideally, a psychologist conducts a manual risk assessment using standard psychological assessment tools, which has long been regarded as a standard method for recidivism risk assessment. However, such behavioral examination procedures are usually sluggish and constrained by subjective perceptions. Consequently, this study aims to develop a machine learning-based quantitative risk assessment tool for the recidivism behavioral gradation of first-time offenders. Quantitative gradation and prediction of future recidivism behavior in such offenders are achieved using an ensemble learning model and an advanced machine-learning approach. For the available behavioral data collected from multiple prison locations, simulations were performed, and the experimental results were obtained. It is ascertained that, the proposed three-member and five-member ensemble classifier models lead to 85.47% and 87.72% accuracy respectively in comparison to other standard individual classifiers.