Title: Preventing crimes ahead of time by predicting crime propensity in released prisoners using data mining techniques
Authors: H. Benjamin Fredrick David; A. Suruliandi; S.P. Raja
Addresses: Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India ' Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India ' Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India
Abstract: Criminologists and psychologists around the world are finding new initiatives to identify criminals and understand crime scenes. This work focuses on predicting the occurrence of crimes for a released prisoner, based on crime propensity prediction, using a supervised machine learning technique. This original research is intended to design and develop a new dataset of 30 attributes that exists nowhere and is exclusively created to define prisoners so as to differentiate them by their propensity to crime using psychological and behavioural factors obtained from jails and assorted sources. The research incorporates an analysis of seven search methods, in tandem with seven subset evaluation techniques, to undertake feature selection, and nine classification algorithms for the classification of prisoners. It is found that the wolf search algorithm, used with the correlation-based feature subset evaluation technique and radial basis function classifier, performs best providing 97.8% precision, 97.5% recall and low error values.
Keywords: crime analysis; prediction; crime prediction; prisoner analysis; prison justice; wolf search algorithm; WSA; RBF classifier; classification; data mining.
International Journal of Applied Decision Sciences, 2019 Vol.12 No.3, pp.307 - 336
Available online: 19 Mar 2019 *Full-text access for editors Access for subscribers Purchase this article Comment on this article