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A similarity measure to assess the stability of classification trees

Author

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  • Briand, Bénédicte
  • Ducharme, Gilles R.
  • Parache, Vanessa
  • Mercat-Rommens, Catherine

Abstract

It has been recognized that Classification trees (CART) are unstable; a small perturbation in the input variables or a fresh sample can lead to a very different classification tree. Some approaches exist that try to correct this instability. However, their benefits can, at present, be appreciated only qualitatively. A similarity measure between two classification trees is introduced that can measure their closeness. Its usefulness is illustrated with synthetic data on the impact of radioactivity deposit through the environment. In this context, a modified node level stabilizing technique, referred to as the NLS-REP method, is introduced and shown to be more stable than the classical CART method.

Suggested Citation

  • Briand, Bénédicte & Ducharme, Gilles R. & Parache, Vanessa & Mercat-Rommens, Catherine, 2009. "A similarity measure to assess the stability of classification trees," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1208-1217, February.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:4:p:1208-1217
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    References listed on IDEAS

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    1. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
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    2. Aniek Sies & Iven Mechelen, 2020. "C443: a Methodology to See a Forest for the Trees," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 730-753, October.
    3. Piccarreta, Raffaella, 2010. "Binary trees for dissimilarity data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1516-1524, June.

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