NDLI: Bayesian Network Modeling of Offender Behavior for Criminal Profiling

Content Provider	IEEE Xplore Digital Library
Author	Baumgartner, K.C. Ferrari, S. Salfati, C.G.
Copyright Year	2005
Description	Author affiliation: graduate student of Mechanical Engineering at Duke University, Durham, NC 27707, USA kac20@duke.edu (Baumgartner, K.C.)
Abstract	A Bayesian network (BN) model of criminal behavior is obtained linking the action of an offender on the scene of the crime to his or her psychological profile. Structural and parameter learning algorithms are employed to discover inherent relationships that are embedded in a database containing crime scene and offender characteristics from homicide cases solved by the British police from the 1970s to the early 1990s. A technique has been developed to reduce the search space of possible BN structures by modifying the greedy search K2 learning algorithm to include a-priori conditional independence relations among nodes. The new algorithm requires fewer training cases to build a satisfactory model that avoids zero-marginal-probability (ZMP) nodes. This can be of great benefit in applications where additional data may not be readily available, such as criminal profiling. Once the BN model is constructed, an inference algorithm is used to predict the offender profile from the behaviors observed on the crime scene. The overall model predictive accuracy of the model obtained by the modified K2 algorithm is found to be 79%, showing a 15% improvement with respect to a model obtained from the same data by the original K2 algorithm. This method quantifies the uncertainty associated with its predictions based on the evidence used for inference. In fact, the predictive accuracy is found to increase with the confidence level provided by the BN. Thus, the confidence level provides the user with a measure of reliability for each variable predicted in any given case.
Starting Page	2702
Ending Page	2709
File Size	172069
Page Count	8
File Format	PDF
ISBN	0780395670
DOI	10.1109/CDC.2005.1582571
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-12-15
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	IEEE/EUCA
Subject Keyword	Bayesian methods Layout Psychology Predictive models Inference algorithms Accuracy Mechanical engineering Statistical analysis Joining processes Databases
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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