Discretisation Effects in Naive Bayesian Networks

Naive Bayesian networks are often used for classification problems that involve variables of a continuous nature. Upon capturing such variables, their value ranges are modelled as finite sets of discrete values. While the output probabilities and conclusions established from a Bayesian network are dependent of the actual discretisations used for its variables, the effects of choosing alternative discretisations are largely unknown as yet. In this paper, we study the effects of changing discretisations on the probability distributions computed from a naive Bayesian network. We demonstrate how recent insights from the research area of sensitivity analysis can be exploited for this purpose.