Cost sensitive discretization of numeric attributes

  • Tom Brijs
  • Koen Vanhoof
Communications Session 4. Clustering and Discretization
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1510)


Many algorithms in decision tree learning are not designed to handle numeric 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 transparant description of the method and steps involved in cost sensitive discretization is given. We also evaluate its performance against two other well known methods, i.e. entropy-based discretization and pure error-based discretization on a real life financial dataset. From the algoritmic point of view, we show that an important deficiency from error-based discretization methods can be solved by introducing costs. From the application point of view, we discovered that using a discretization method is recommended. To conclude, we use ROC-curves to illustrate that under particular conditions cost-based discretization may be optimal.


Discretization Method Cost Parameter Minority Class Error Cost Misclassification Cost 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Tom Brijs
    • 1
  • Koen Vanhoof
    • 2
  1. 1.Faculty of Applied Economic SciencesLimburg University CentreDiepenbeekBelgium
  2. 2.Faculty of Applied Economic SciencesLimburg University CentreDiepenbeekBelgium

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