Optimal dyadic decision trees

G. Blanchard; C. Schäfer; Y. Rozenholc; Klaus Muller

doi:10.1007/s10994-007-0717-6

Optimal dyadic decision trees

G. Blanchard, C. Schäfer, Y. Rozenholc, Klaus Muller

Research output: Contribution to journal › Article

24 Citations (Scopus)

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.

Original language	English
Pages (from-to)	209-241
Number of pages	33
Journal	Machine Learning
Volume	66
Issue number	2-3
DOIs	https://doi.org/10.1007/s10994-007-0717-6
Publication status	Published - 2007 Mar 1
Externally published	Yes

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Keywords

Adaptive convergence rate
Classification
Decision tree
Density estimation
Oracle inequality

ASJC Scopus subject areas

Control and Systems Engineering
Artificial Intelligence

Cite this

Blanchard, G., Schäfer, C., Rozenholc, Y., & Muller, K. (2007). Optimal dyadic decision trees. Machine Learning, 66(2-3), 209-241. https://doi.org/10.1007/s10994-007-0717-6

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Access to Document

10.1007/s10994-007-0717-6