Criminal prediction using Naive Bayes theory

Abstract

The paper introduces a solution to the criminal prediction problem using Naive Bayes theory. The criminal prediction problem is stated as finding the most likely criminal of a particular crime incident when the history of crime incidents is given with the incident-level crime data. The incident-level crime data are assumed to be given as a crime dataset where the incident date and location, crime type, criminal ID and the acquaintances are the attributes or crime parameters considered in the paper. The acquaintances are the suspects whose names are either directly involved in the incident or indirectly the acquaintances of the criminal. Acquiring the crime dataset is a difficult process in practice due to confidentiality principle. So the crime dataset is generated synthetically using the state-of-the-art methods. The proposed system is tested for the criminal prediction problem using the cross-validation, and the experimental results show that the proposed system provides high scores in finding of suspected criminals.