犯罪者プロファイリングにおけるベイジアンネットワーク活用の有効性についての一考察
─屋内強姦における犯人の職業に関する確率的推定─

抄録

  Offender profiling is one of the tools of decision making for criminal investigation. It is a set of techniques to infer characteristics of an unknown offender, such as sex, age bracket, lifestyle, psychological feature, previous crime, inhabited area, from the information which is left at the crime scene.
  In this article, we proposed a tool of decision-making for criminal investigation from the perspective of prediction of an uncertain event by the use of a Bayesian Network (BN). BN is a probability model that describes causal structure of events as chain networks of conditional probability, and is capable to predict the possibility of uncertain events.
  To examine the validity of the constructed model, firstly, we divided previous offenders’ information of the indoor-sex-offence cases into a training data (9,859 cases) and validation data (50 cases). Secondly, we constructed a model from the training data by means of K2 and MDL (minimum description length) as search-algorithm and information criteria, respectively. Finally, the validity of the model was examined by the validation data as virtual cases.
  According to the model, 21 target variables (16 behavioral variables, 2 vehicle variables and 3 victim variables) linked the explanatory variable (employment) directly, and most of these variables related to the employment. The results of the model validity showed that the accuracy of predicting the employment increased 10% higher when the age bracket could be estimated from the testimony of the victim.
  The results indicated that the BN model of the offender profiling would be able to provide valuable information for decision making for crime investigation. To predict characteristics of an unknown offender more accurately, it is crucial to select more appropriate information criteria and develop the search-algorithm, as well as to construct the database from more accurate information.