Constructing Bayesian networks for criminal profiling from limited data

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%.

[1]  David Canter,et al.  Classifying homicide offenders and predicting their characteristics from crime scene behavior. , 2003, Scandinavian journal of psychology.

[2]  Kuo-Chu Chang,et al.  Comparison of score metrics for Bayesian network learning , 2002, IEEE Trans. Syst. Man Cybern. Part A.

[3]  Michael I. Jordan,et al.  Probabilistic Networks and Expert Systems , 1999 .

[4]  Jing Zhou,et al.  Learning Bayesian networks with a hybrid convergent method , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[5]  Ann Wolbert Burgess,et al.  Sexual Homicide: Patterns and Motives , 1988 .

[6]  Gregory F. Cooper,et al.  Model Averaging for Prediction with Discrete Bayesian Networks , 2004, J. Mach. Learn. Res..

[7]  Peter J. F. Lucas,et al.  Employing Maximum Mutual Information for Bayesian Classification , 2004, ISBMDA.

[8]  Norman J. Finkel,et al.  Criminal personality profiling , 1990 .

[9]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[10]  D. Heckerman,et al.  Toward Normative Expert Systems: Part I The Pathfinder Project , 1992, Methods of Information in Medicine.

[11]  Richard N Kocsis,et al.  Offender profiling: An introduction to the sociopsychological analysis of violent crime. , 2005 .

[12]  S. Ferrari,et al.  Demining sensor modeling and feature-level fusion by Bayesian networks , 2006, IEEE Sensors Journal.

[13]  Richard N Kocsis,et al.  Psychological Profiling of Offender Characteristics from Crime Behaviors in Serial Rape Offences , 2002, International journal of offender therapy and comparative criminology.

[14]  K.C. Baumgartner,et al.  Bayesian Network Modeling of Offender Behavior for Criminal Profiling , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[15]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[16]  Robert Cowell,et al.  Advanced Inference in Bayesian Networks , 1999, Learning in Graphical Models.

[17]  Norman E. Fenton,et al.  1 2 3 4 5 6 7 , 2001 .

[18]  Richard N Kocsis,et al.  Criminal Profiling: Principles and Practice , 2006 .

[19]  Steven A. Egger.  Psychological Profiling , 1999 .

[20]  Robert Cowell,et al.  Introduction to Inference for Bayesian Networks , 1998, Learning in Graphical Models.

[21]  C. Gabrielle Salfati,et al.  Offender Interaction With Victims in Homicide , 2003 .

[22]  Bruce Abramson.  ARCO1: An Application of Belief Networks to the Oil Market , 1991, UAI.

[23]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[24]  Sven Wachsmuth,et al.  Modelling Expertise for Structure Elucidation in Organic Chemistry Using Bayesian Networks , 2004, SGAI Conf..

[25]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[26]  C. Gabrielle Salfati,et al.  The Nature of Expressiveness and Instrumentality in Homicide , 2000 .

[27]  Pedro Larrañaga,et al.  Learning Bayesian network structures by searching for the best ordering with genetic algorithms , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[28]  Nir Friedman,et al.  Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm , 1999, UAI.

[29]  Petter Gottschalk.  Stages of knowledge management systems in police investigations , 2006, Knowl. Based Syst..

[30]  Craig Bennell,et al.  THE PERSONALITY PARADOX IN OFFENDER PROFILING A Theoretical Review of the Processes Involved in Deriving Background Characteristics From Crime Scene Actions , 2002 .

[31]  Byoung-Tak Zhang,et al.  Bayesian model averaging of Bayesian network classifiers over multiple node-orders: application to sparse datasets , 2005, IEEE Trans. Syst. Man Cybern. Part B.

[32]  D. Rossmo,et al.  Geographic profiling : target patterns of serial murderers , 1995 .

[33]  Gregory F. Cooper,et al.  A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .

[34]  Marco Strano.  A Neural Network Applied to Criminal Psychological Profiling: An Italian Initiative , 2004, International journal of offender therapy and comparative criminology.

[35]  V. Austin,et al.  Match'em: using fuzzy logic to profile criminals , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[36]  Nir Friedman,et al.  Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks , 2004, Machine Learning.

[37]  David Heckerman,et al.  A Bayesian Approach to Learning Causal Networks , 1995, UAI.

[38]  D V Canter,et al.  Differentiating stranger murders: profiling offender characteristics from behavioral styles. , 1999, Behavioral sciences & the law.

[39]  Wray L. Buntine.  A Guide to the Literature on Learning Probabilistic Networks from Data , 1996, IEEE Trans. Knowl. Data Eng..

[40]  J.A. Lozano,et al.  Bayesian Model Averaging of Naive Bayes for Clustering , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[41]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[42]  John W. Brahan,et al.  AICAMS: artificial intelligence crime analysis and management system , 1998, Knowl. Based Syst..

[43]  Daniel Nikovski,et al.  Constructing Bayesian Networks for Medical Diagnosis from Incomplete and Partially Correct Statistics , 2000, IEEE Trans. Knowl. Data Eng..