Optimal dyadic decision trees

  • Authors:
  • G. Blanchard;C. Schäfer;Y. Rozenholc;K.-R. Müller

  • Affiliations:
  • Fraunhofer First (IDA), Berlin, Germany D-12489;Fraunhofer First (IDA), Berlin, Germany D-12489;Applied Mathematics Department (MAP5), Université René Descartes, Paris Cedex, France 75270;Computer Science Department, Technical University of Berlin, Franklinstr. 28/29, 10587 Berlin, Germany

  • Venue:
  • Machine Learning
  • Year:
  • 2007

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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.