The usability of stacking-based ensemble learning model in crime prediction: a systematic review

This research addresses the potential for tackling crime volumes and improving crime analytics through new enhancement strategies. The use of machine learning and deep learning solutions is increasing in crime prediction, as in many other fields. This study aims to strengthen proactive approaches in criminology by evaluating the effectiveness of the stacking-based ensemble learning (S-BEL) model, which aims to enhance overall performance by combining the strengths of various algorithms to improve crime analytics and facilitate crime prevention strategies. The study analyzes six studies leveraging the S-BEL model for crime prediction, along with 28 research articles on crime prediction, seven studies utilizing ensemble learning models, and 56 research articles leveraging the S-BEL model in general prediction studies. The findings of the study highlight that S-BEL stands out as a prominent technique in crime prediction, providing valuable insights for law enforcement.