On the Boosting Ability of Top-Down Decision Tree Learning Algorithms (1995)
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| Venue: | In Proceedings of the Twenty-Eighth Annual ACM Symposium on the Theory of Computing |
| Citations: | 81 - 6 self |
BibTeX
@INPROCEEDINGS{Kearns95onthe,
author = {Michael Kearns and Yishay Mansour},
title = {On the Boosting Ability of Top-Down Decision Tree Learning Algorithms},
booktitle = {In Proceedings of the Twenty-Eighth Annual ACM Symposium on the Theory of Computing},
year = {1995},
pages = {459--468},
publisher = {ACM Press}
}
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Abstract
We analyze the performance of top-down algorithms for decision tree learning, such as those employed by the widely used C4.5 and CART software packages. Our main result is a proof that such algorithms are boosting algorithms. By this we mean that if the functions used to label the internal nodes of the decision tree can weakly approximate the unknown target function, then the top-down algorithms we study will amplify this weak advantage to build a tree achieving any desired level of accuracy. The bounds we obtain for this amplification show an interesting dependence on the splitting criterion function G used by the top-down algorithm. More precisely, if the functions used to label the internal nodes have error 1=2 \Gamma fl as approximations to the target function, then for the splitting criteria used by CART and C4.5, trees of size (1=ffl) O(1=fl 2 ffl 2 ) and (1=ffl) O(log(1=ffl)=fl 2 ) (respectively) suffice to drive the error below ffl. Thus, small constant advantage over...
