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 |
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Pages (from-to) | 209-241 |
Number of pages | 33 |
Journal | Machine Learning |
Volume | 66 |
Issue number | 2-3 |
DOIs | |
Publication status | Published - 2007 Mar |
Keywords
- Adaptive convergence rate
- Classification
- Decision tree
- Density estimation
- Oracle inequality
ASJC Scopus subject areas
- Software
- Artificial Intelligence