Cost Sensitive Discretization of Numeric Attributes

Many algorithms in decision tree learning have not been designed to handle numerically-valued attributes very well. Therefore, discretization of the continuous feature space has to be carried out. In this article we introduce the concept of cost-sensitive discretization as a preprocessing step to induction of a classifier and as an elaboration of the error-based discretization method to obtain an optimal multi-interval splitting for each numeric attribute. A transparent description of the method and steps involved in cost-sensitive discretization is given. We also provide an assessment of its performance against two other well-known methods, i.e. entropy-based discretization and pure error-based discretization on an authentic financial dataset. From an algorithmic perspective, we show that an important deficiency of error-based discretization methods can be solved by introducing costs. From the application perspective, we discovered that using a discretization method is recommended. Finally, we use ROC-curves to illustrate that under particular conditions cost-based discretization can be optimal.