ML Anthology

Optimal Dyadic Decision Trees

Gilles Blanchard, Christin Schäfer, Yves Rozenholc, Klaus-Robert Müller
MLJ 2007 pp. 209-241
doi:10.1007/S10994-007-0717-6 /mlj/2007/blanchard2007mlj-optimal/

Abstract

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.

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Text

Blanchard et al. "Optimal Dyadic Decision Trees." Machine Learning, 2007. doi:10.1007/S10994-007-0717-6

Markdown

[Blanchard et al. "Optimal Dyadic Decision Trees." Machine Learning, 2007.](https://mlanthology.org/mlj/2007/blanchard2007mlj-optimal/) doi:10.1007/S10994-007-0717-6

BibTeX

@article{blanchard2007mlj-optimal,
  title     = {{Optimal Dyadic Decision Trees}},
  author    = {Blanchard, Gilles and Schäfer, Christin and Rozenholc, Yves and Müller, Klaus-Robert},
  journal   = {Machine Learning},
  year      = {2007},
  pages     = {209-241},
  doi       = {10.1007/S10994-007-0717-6},
  volume    = {66},
  url       = {https://mlanthology.org/mlj/2007/blanchard2007mlj-optimal/}
}