ML Anthology

A Tight Integration of Pruning and Learning (Extended Abstract)

Johannes Fürnkranz
ECML-PKDD 1995 pp. 291-294
doi:10.1007/3-540-59286-5_70 /ecmlpkdd/1995/furnkranz1995ecml-tight/

Abstract

This paper outlines some problems that may occur with Reduced Error Pruning in rule learning algorithms. In particular we show that pruning complete theories is incompatible with the separate-and-conquer learning strategy that is commonly used in propositional and relational rule learning systems. As a solution we propose to integrate pruning into learning and examine two algorithms, one that prunes at the clause level and one that prunes at the literal level. Experiments show that these methods are not only much more efficient, but also able to achieve small gains in accuracy by solving the outlined problem.

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Cite

Text

Fürnkranz. "A Tight Integration of Pruning and Learning (Extended Abstract)." European Conference on Machine Learning, 1995. doi:10.1007/3-540-59286-5_70

Markdown

[Fürnkranz. "A Tight Integration of Pruning and Learning (Extended Abstract)." European Conference on Machine Learning, 1995.](https://mlanthology.org/ecmlpkdd/1995/furnkranz1995ecml-tight/) doi:10.1007/3-540-59286-5_70

BibTeX

@inproceedings{furnkranz1995ecml-tight,
  title     = {{A Tight Integration of Pruning and Learning (Extended Abstract)}},
  author    = {Fürnkranz, Johannes},
  booktitle = {European Conference on Machine Learning},
  year      = {1995},
  pages     = {291-294},
  doi       = {10.1007/3-540-59286-5_70},
  url       = {https://mlanthology.org/ecmlpkdd/1995/furnkranz1995ecml-tight/}
}