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 that label the internal nodes of the
Top-down induction of first-order logical decision trees
✍ Scribed by Hendrik Blockeel; Luc De Raedt
- Publisher
- Elsevier Science
- Year
- 1998
- Tongue
- English
- Weight
- 895 KB
- Volume
- 101
- Category
- Article
- ISSN
- 0004-3702
No coin nor oath required. For personal study only.
✦ Synopsis
A first-order framework for top-down induction of logical decision trees is introduced. The expressivity of these trees is shown to be larger than that of the flat logic programs which are typically induced by classical ILP systems, and equal to that of first-order decision lists. These results are related to predicate invention and mixed variable quantification. Finally, an implementation of this framework, the TILDE system, is presented and empirically evaluated.
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