Optimal dyadic decision trees

G. Blanchard; C. Schäfer; Y. Rozenholc; K. R. Müller

doi:10.1007/s10994-007-0717-6

Optimal dyadic decision trees

G. Blanchard, C. Schäfer, Y. Rozenholc, K. R. Müller

Research output: Contribution to journal › Article › peer-review

30 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

Bibliographical note

Funding Information:
Acknowledgments This work is partly funded by an grant of the Alexander von Humboldt Foundation, the PASCAL Network of Excellence (EU # 506778), and the Bundesministerium für Bildung und Forschung FKZ 01—BB02A and FKZ 01-SC40A. The authors thank Mikio Braun for valuable discussions, Nicolas Heefl for helping us with automatic tree drawing, and Alexander Binder for running the experiments again in Section 4.2 for the revision of the paper.

Keywords

Adaptive convergence rate
Classification
Decision tree
Density estimation
Oracle inequality

ASJC Scopus subject areas

Software
Artificial Intelligence

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10.1007/s10994-007-0717-6

Cite this

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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.",

keywords = "Adaptive convergence rate, Classification, Decision tree, Density estimation, Oracle inequality",

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note = "Funding Information: Acknowledgments This work is partly funded by an grant of the Alexander von Humboldt Foundation, the PASCAL Network of Excellence (EU # 506778), and the Bundesministerium f{\"u}r Bildung und Forschung FKZ 01—BB02A and FKZ 01-SC40A. The authors thank Mikio Braun for valuable discussions, Nicolas Heefl for helping us with automatic tree drawing, and Alexander Binder for running the experiments again in Section 4.2 for the revision of the paper.",

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doi = "10.1007/s10994-007-0717-6",

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AU - Blanchard, G.

AU - Schäfer, C.

AU - Rozenholc, Y.

AU - Müller, K. R.

N1 - Funding Information: Acknowledgments This work is partly funded by an grant of the Alexander von Humboldt Foundation, the PASCAL Network of Excellence (EU # 506778), and the Bundesministerium für Bildung und Forschung FKZ 01—BB02A and FKZ 01-SC40A. The authors thank Mikio Braun for valuable discussions, Nicolas Heefl for helping us with automatic tree drawing, and Alexander Binder for running the experiments again in Section 4.2 for the revision of the paper.

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AB - 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.

KW - Adaptive convergence rate

KW - Classification

KW - Decision tree

KW - Density estimation

KW - Oracle inequality

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EP - 241

JO - Machine Learning

JF - Machine Learning

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ER -

Optimal dyadic decision trees

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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