A Wrapper Evolutionary Approach for Supervised Multivariate Discretization: A Case Study on Decision Trees

  • Sergio Ramírez-Gallego
  • Salvador García
  • José Manuel Benítez
  • Francisco Herrera
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 403)


The main objective of discretization is to transform numerical attributes into discrete ones. The intention is to provide the possibility to use some learning algorithms which require discrete data as input and to help the experts to understand the data more easily. Due to the fact that in classification problems there are high interactions among multiple attributes, we propose the use of evolutionary algorithms to select a subset of cut points for multivariate discretization based on a wrapper fitness function. The algorithm proposed has been compared with the best state-of-the-art discretizers with two decision trees-based classifiers: C4.5 and PUBLIC. The results reported indicate that our proposal outperforms the rest of the discretizers in terms of accuracy and requiring a lower number of intervals.


Discretization Numerical attributes Evolutionary algorithms Data preprocessing Classification 



This work was partially supported by the Spanish Ministry of Science and Technology under project TIN2011-28488 and the Andalusian Research Plans P11-TIC-7765, P10-TIC-6858.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sergio Ramírez-Gallego
    • 1
  • Salvador García
    • 1
  • José Manuel Benítez
    • 1
  • Francisco Herrera
    • 1
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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