Hybrid machine learning methods for risk assessment in gender-based crime

Abstract

Gender-based crime is one of the most concerning scourges of contemporary society, and governments worldwide have invested lots of economic and human resources to foretell their occurrence and anticipate the aggressions. In this work, we propose to apply Machine Learning (ML) techniques to create models that accurately predict the recidivism risk of a gender-violence offender. We feed the model with data extracted from the official Spanish VioGen system and comprising more than 40,000 reports of gender violence. To evaluate the performance, two new quality measures are proposed to assess the effective police protection that a model supplies and the overload in the invested resources that it generates. The empirical results show a clear outperformance of the ML-centered approach, with an improvement of up to a 25% with respect to the preexisting risk assessment system. Additionally, we propose a hybrid model that combines the statistical prediction methods with the ML method, permitting authorities to implement a smooth transition from the preexisting model to the ML-based model. To the best of our knowledge, this is the first work that achieves an effective ML-based prediction for this type of crimes against an official dataset.