A comparison of methods for discretizing continuous variables in Bayesian Networks. (October 2018) - Beuzen, Tomas; Marshall, Lucy; Splinter, Kristen D.
Abstract: Bayesian Networks (BNs) are an increasingly popular method for modelling environmental systems. The discretization of continuous variables is often required to use BNs. There are three main methods of discretization; manual, unsupervised, and supervised. Here, we compare and demonstrate each approach with a BN that predicts coastal erosion. Results reveal that supervised discretization methods produced BNs of the highest average predictive skill (73.8%), followed by manual discretization (69.0%) and unsupervised discretization (64.8%). However, each method has specific advantages that may make them more suitable for particular applications. Manual methods can produce physical meaningful BNs, which is favorable in environmental modelling. Supervised methods can autonomously and optimally discretize variables and may be preferred when predictive skill is a modelling priority. Unsupervised methods are computationally simple and versatile. The optimal discretization scheme should consider both the performance and practicality of the scheme. Highlights: Manual, unsupervised and supervised discretization methods are used to create a Bayesian Network modelling coastal erosion. The method of discretization used affected BN model performance. Supervised discretization methods produced the best performing BNs. Each method has other advantages unrelated to model performance, e.g., physical interpretation or computational efficiency.