Abstract

We describe a new method for multivariate discretization based on the use of a latent variable model. The method is proposed as a tool to extend the scope of applicability of machine learning algorithms that handle discrete variables only. 1 Introduction The discretization of continuous variables arises as an issue in machine learning because of the availability of machine learning algorithms that can handle discrete variables only. Furthermore, even when the learning algorithm at hand can directly model continuous variables, it may be possible to improve its predictive performance, as well as its induction time, by using discretized variables [6, 10]. While many of the available discretization algorithms search for the best discretization of each continuous variable individually, an approach that we refer to as univariate discretization, ideally the discretization of a continuous variable should be carried out so as to minimize the loss of information that the given variable may con...

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