Optimal dyadic decision trees

G. Blanchard, C. Schäfer, Y. Rozenholc, K. R. Müller

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


We introduce a new algorithm building an optimal dyadic decision tree (ODT). The method combines guaranteed performance in the learning theoretical sense and optimal search from the algorithmic point of view. Furthermore it inherits the explanatory power of tree approaches, while improving performance over classical approaches such as CART/C4.5, as shown on experiments on artificial and benchmark data.

Original languageEnglish
Pages (from-to)209-241
Number of pages33
JournalMachine Learning
Issue number2-3
Publication statusPublished - 2007 Mar


  • Adaptive convergence rate
  • Classification
  • Decision tree
  • Density estimation
  • Oracle inequality

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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