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 | |
| 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
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 journal › Article
}
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 -