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Optimal dyadic decision trees

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


Machine learning 66 (2007), No.2-3, pp.209-241
ISSN: 0885-6125
Journal Article
Fraunhofer FIRST ()

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.