Currently, criminals profile (CP) is obtained from investigators or forensic psychologists interpretation, linking crime scene characteristics and an offenders behavior to his or her characteristics and psychological profile. This paper seeks an efficient and systematic discovery of nonobvious and valuable patterns between variables from a large database of solved cases via a probabilistic network (PN) modeling approach. The PN structure can be used to extract behavioral patterns and to gain insight into what factors influence these behaviors. Thus, when a new case is being investigated and the profile variables are unknown because the offender has yet to be identified, the observed crime scene variables are used to infer the unknown variables based on their connections in the structure and the corresponding numerical (probabilistic) weights. The objective is to produce a more systematic and empirical approach to profiling, and to use the resulting PN model as a decision tool.
Deep Dive into Modeling of Human Criminal Behavior using Probabilistic Networks.
Currently, criminals profile (CP) is obtained from investigators or forensic psychologists interpretation, linking crime scene characteristics and an offenders behavior to his or her characteristics and psychological profile. This paper seeks an efficient and systematic discovery of nonobvious and valuable patterns between variables from a large database of solved cases via a probabilistic network (PN) modeling approach. The PN structure can be used to extract behavioral patterns and to gain insight into what factors influence these behaviors. Thus, when a new case is being investigated and the profile variables are unknown because the offender has yet to be identified, the observed crime scene variables are used to infer the unknown variables based on their connections in the structure and the corresponding numerical (probabilistic) weights. The objective is to produce a more systematic and empirical approach to profiling, and to use the resulting PN model as a decision tool.
Modeling human criminal behavior is challenging due to many variables involved and the high degree of uncertainty surrounding a criminal act and the corresponding investigation. Probabilistic graphs are suitable modeling techniques because they are inherently distributed and stochastic. In this paper, the system variables comprising the PN are offender behaviors and crime scene evidence, which are initialized by experts through their professional experience or expert knowledge.
The mathematical relationships naturally embedded in a set of crimes [3,4,8] are learned through training from a database containing solved criminal cases. The PN model is to be applied when only the crime scene evidence is known to obtain a useable offender profile to aid law enforcement in the investigations. A criminal profile is predicted with a certain quantitative confidence.
The PN approach presented here seeks to build on the ideas of behavior correlations in order to obtain a usable criminal profile when only crime scene evidence is known from the investigation.
This paper proposes a systematic approach for deriving a multidisciplinary behavioral model of criminal behavior. The proposed offender behavioral model is a mathematical representation of a system comprised of an offender’s actions and decisions at a crime scene and the offender’s personal characteristics.
The influence of the offender traits and characteristics on the resulting crime scene behaviors is captured by a probabilistic graph or PN that maps cause-and-effect relationships between events, and lends itself to inductive logic for reasoning under uncertainty [1]. The use of PNs for CP may allow investigators to take into consideration various aspects of the crime and discover behavioral patterns that might otherwise remain hidden in the data. The various aspects of a crime include a victimology assessment (victim’s characteristics, e.g., background characteristics, age, gender, and education), crime scene analysis (evidence from the crime scene, e.g., time and place where the crime occurred), and a medical report (autopsy report, e.g., type of non-deadly and deadly lesions and signs of self defense).
The PN approach to criminal profiling is demonstrated by learning from a series of crime scene and offender behaviors. The learning techniques employed in this modeling research are evaluated on a set of validation cases not used for training by defining a prediction accuracy based on the most likely value of the output variables (offender profile) and its corresponding confidence level.
To start with, a graphical model of offender behavior is learned from a database of solved cases. The resulting CP model obtained through training is then tested by comparing its predictions to the actual offenders’ profiles.
Let the database sample space = D, Let D consist of ’d’ solved cases {C 1 , …,C d }, where C i is an instantiation of X, which is randomly partitioned into two independent datasets such as a training set T and a validation set V, such that D = T U V. The variables in X are partitioned as follows: the inputs are the crime scene (CS) variables X I (evidence) for X I = (X I 1 , …,X I k ), and the outputs are the offender (OFF) variables comprising the criminal profile X O , for X O = (X O 1 , …,X O m ), where (X I ,X O ) ε X.
The PN model is learned from T, as explained later, and it is tested by performing inference to predict the offender variables (OFF) in the validation cases V. An offender profile is estimated based on crime scene evidence, with a prediction being the most likely value of a particular offender variable. During the testing phase, the predicted value of X O i , denoted by x P i,a where a=1 or 2 for a binary variable, is compared to the observed state x O i,b obtained from the validation set V, where b=1 or 2. An example of an offender variable is “gender”, with states “male” and “female”. The overall performance of the PN model is evaluated by comparing the true (observed) states x O i,b to the predicted output variable values x P i,a in the validation cases. This process tests the generalization properties of the model by evaluating its efficiency over V.
The relevant categories of variables that have emerged from the criminal profiling research as selected by investigators, criminologists, and forensic psychologists are described as follows:
• Crime Scene Analysis (CSA): CSA variables are systematic observations made at the crime scene by the investigator. Examples of CSA variable pertain to where the body was found (e.g., neighborhood, location, environment characteristics), how the victim was found (e.g., the body was well-hidden, partially hidden, or intentionally placed for discovery), and the correlation between where the crime took place and where the body was found (e.g., the body was transported after the murder).
• Victimology Analysis (VA): VA variables consist of the background characteristics of the victim inde
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