A comparative analysis of Bayesian network structure learning algorithms applied to crime data

Autor(es)

Fazanaro, Dalton Ieda ^[1] ; Pedrini, Helio ^[1]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Intelligent Data Analysis; v. 24, n. 4, p. 887-907, 2020.
• Citações Web of Science: 0

Resumo

The theories about crime and correction have their inception in the eighteenth century, highly influenced by the anthropological thoughts emerging during the age of Enlightenment. Throughout the decades, the criminological studies observed their sociological essence encompassing practices from other scientific fields to explain the more contemporary questions, becoming Criminology an inherently interdisciplinary science as a result. The adoption of concepts from Exact Sciences is a recent moving, originating it a novel research area, called Computational Criminology, which employs procedures from Applied Mathematics, Statistics and Computer Science to provide original or enhanced solutions to such questions. One of the most prominent tasks brought by this rising field is crime prediction, which attempts to uncover potential targets for future police intervention and also help solving already committed offenses. The present comparative analysis thus investigates the employment of statistical inference by means of Bayesian network for predictive policing, using the openly accessible registers from Chicago Police Department. Numerous algorithms are available to learn the structure for a Bayesian network purely from data and a comparative examination about them is hence described, with the purpose to establish the most precise and efficient one, according to the attributes of the said criminal dataset, for the implementation of the intended inference. (AU)

• Processo FAPESP: 17/02073-6 - Modelo de redes para predição de crimes
• Beneficiário: Dalton Ieda Fazanaro
• Modalidade de apoio: Bolsas no Brasil - Doutorado