Constructing Bayesian networks for criminal profiling from limited data

被引：40

作者：

Baumgartner, K. ^{[1
]}

Ferrari, S. ^{[1
]}

Palermo, G. ^{[2
]}

机构：

[1] Duke Univ, Pratt Sch Engn, Durham, NC 27708 USA

[2] Med Coll Wisconsin, Dept Psychiat & Neurol, Milwaukee, WI 53226 USA

来源：

KNOWLEDGE-BASED SYSTEMS | 2008年 / 21卷 / 07期

关键词：

Criminal profiling; Crime analysis; Automation; Bayesian networks; Performance metrics;

D O I：

10.1016/j.knosys.2008.03.019

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The increased availability of information technologies has enabled law enforcement agencies to compile databases with detailed information about major felonies. Machine learning techniques can utilize these databases to produce decision-aid tools to support police investigations. This paper presents a methodology for obtaining a Bayesian network (BN) model of offender behavior from a database of cleared homicides. The BN can infer the characteristics of an unknown offender from the crime scene evidence and, help narrow the list of suspects in an unsolved homicide. Our research shows that 80% of offender characteristics are predicted correctly on average in new single-victim homicides, and when confidence levels are taken into account this accuracy increases to 95.6%. (c) 2008 Elsevier B.V. All rights reserved.

引用

页码：563 / 572

页数：10

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