Open AccessA Comparative Analysis of Artificial Intelligence Techniques in Forecasting Violent Crime Rate
Author(s) -
Azhar Khairuddin,
Razana Alwee,
Habibollah Haron
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/864/1/012056
Subject(s) - artificial neural network , law enforcement , gradient boosting , decision tree , boosting (machine learning) , computer science , artificial intelligence , globe , crime analysis , crime rate , support vector machine , violent crime , machine learning , criminology , random forest , law , political science , psychology , neuroscience
The increase in the occurrence of violent crimes is a major concern in all countries around the globe. Various approaches of crime analyses have been implemented in reducing the number of violent crimes and among them is crime forecasting. Crime forecasting is an effective solution as it assists law enforcement agencies in planning efficient crime prevention measures. It has been observed recently that the application of artificial intelligence (AI) techniques in crime forecasting and analysis is favoured by researchers. Motivated by this development, this study aims to conduct a comparative analysis on the forecasting performance of three artificial intelligence (AI) techniques, namely artificial neural network (ANN), support vector regression (SVR), and gradient tree boosting (GTB) in forecasting the rates of four types of crimes in the United States (US). The forecasting performance of each AI technique was compared in terms of quantitative error measurement. From the results obtained, GTB showed the highest forecast accuracy compared to ANN and SVR as the observed error measurements were the smallest.