A Bayesian approach for supervised discretization

In supervised machine learning, some algorithms are restricted to discrete data and thus need to discretize continuous attributes. In this paper, we present a new discretization method called MODL, based on a Bayesian approach. The MODL method relies on a model space of discretizations and on a prior distribution defined on this model space. This allows the setting up of an evaluation criterion of discretization, which is minimal for the most probable discretization given the data, i.e. the Bayes optimal discretization. We compare this approach with the MDL approach and statistical approaches used in other discretization methods, from a theoretical and experimental point of view. Extensive experiments show that the MODL method builds high quality discretizations.