Exploring Bayesian belief network for risky behavior modelling: discretization and latent variables
Decision making in many areas is based on data about individual behavior often measured using different surveys. The study investigates the proposed approach for behavior modelling on the base of Bayesian belief networks that allows predicting behavior characteristics using small and incomplete data from surveys about behavior episodes. We explored the characteristics of the models using the automatically generated dataset that included 44350 cases. During our experiment, we considered three different model structures and compared three different discretization strategies. We found that simpler structures showed better prediction quality for all measures (average accuracy, precision, recall, F1 score). The observed difference was statistically significant but did not exceed 1% that can be considered unimportant if error price is low. Our findings suggested that ways of data transformation, particularly discretization strategies for input data, had a significant impact on prediction quality: background knowledge about distributions, theoretical assumptions about behavior led to higher prediction quality.