Optimal dyadic decision trees

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

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)209-241
Number of pages33
JournalMachine Learning
Volume66
Issue number2-3
DOIs
Publication statusPublished - 2007 Mar 1
Externally publishedYes

Fingerprint

Decision trees
Experiments

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

Optimal dyadic decision trees. / Blanchard, G.; Schäfer, C.; Rozenholc, Y.; Muller, Klaus.

In: Machine Learning, Vol. 66, No. 2-3, 01.03.2007, p. 209-241.

Research output: Contribution to journalArticle

Blanchard, G, Schäfer, C, Rozenholc, Y & Muller, K 2007, 'Optimal dyadic decision trees', Machine Learning, vol. 66, no. 2-3, pp. 209-241. https://doi.org/10.1007/s10994-007-0717-6
Blanchard G, Schäfer C, Rozenholc Y, Muller K. Optimal dyadic decision trees. Machine Learning. 2007 Mar 1;66(2-3):209-241. https://doi.org/10.1007/s10994-007-0717-6
Blanchard, G. ; Schäfer, C. ; Rozenholc, Y. ; Muller, Klaus. / Optimal dyadic decision trees. In: Machine Learning. 2007 ; Vol. 66, No. 2-3. pp. 209-241.
@article{5e5b3951f2854ec788f81dd55b7e07f2,
title = "Optimal dyadic decision trees",
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",
author = "G. Blanchard and C. Sch{\"a}fer and Y. Rozenholc and Klaus Muller",
year = "2007",
month = "3",
day = "1",
doi = "10.1007/s10994-007-0717-6",
language = "English",
volume = "66",
pages = "209--241",
journal = "Machine Learning",
issn = "0885-6125",
publisher = "Springer Netherlands",
number = "2-3",

}

TY - JOUR

T1 - Optimal dyadic decision trees

AU - Blanchard, G.

AU - Schäfer, C.

AU - Rozenholc, Y.

AU - Muller, Klaus

PY - 2007/3/1

Y1 - 2007/3/1

N2 - 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.

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

UR - http://www.scopus.com/inward/record.url?scp=33847623752&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33847623752&partnerID=8YFLogxK

U2 - 10.1007/s10994-007-0717-6

DO - 10.1007/s10994-007-0717-6

M3 - Article

AN - SCOPUS:33847623752

VL - 66

SP - 209

EP - 241

JO - Machine Learning

JF - Machine Learning

SN - 0885-6125

IS - 2-3

ER -