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Sampling Strategies for Targeting Rare Groups from a Bank Customer Database

  • J-H. Chauchat
  • R. Rakotomalala
  • D. Robert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1910)

Abstract

This paper presents various balanced sampling strategies for building decision trees in order to target rare groups. A new coeficient to compare targeting performances of various learning strategies is introduced. A real life application of targeting specific bank customer group for marketing actions is described. Results shows that local sampling on the nodes while constructing the tree requires small samples to achieve the performance of processing the complete base, with dramatically reduced computing times.

Keywords

sampling customer targeting targeting quality coeffcient imbalanced database decision tree application 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • J-H. Chauchat
    • 1
  • R. Rakotomalala
    • 1
  • D. Robert
    • 2
  1. 1.ERIC LaboratoryUniversity of Lyon 2BronFRANCE
  2. 2.Crédit Agricole Centre-EstChampagne aux Monts d’OrFRANCE

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