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Optimal Dyadic Decision Trees
Gilles Blanchard, Christin Schaefer, Yves Rozenholc and Klaus-Robert Müller
Machine Learning
Volume 66,
Number 2-3,
,
2007.
AbstractWe introduce a novel algorithm for optimal dyadic decision
trees (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 not losing too much classification accuracy when
compared to advanced kernel based learning methods. Experiments on
artificial and benchmark data underline the versatility and quality
of our new method. [Edit]
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