Pruning Algorithms for Rule Learning
Abstract
Pre-pruning and Post-pruning are two standard techniques for handling noise in decision tree learning. Pre-pruning deals with noise during learning, while post-pruning addresses this problem after an overfitting theory has been learned. We first review several adaptations of pre- and post-pruning techniques for separate-and-conquer rule learning algorithms and discuss some fundamental problems. The primary goal of this paper is to show how to solve these problems with two new algorithms that combine and integrate pre- and post-pruning.
Cite
Text
Fürnkranz. "Pruning Algorithms for Rule Learning." Machine Learning, 1997. doi:10.1023/A:1007329424533Markdown
[Fürnkranz. "Pruning Algorithms for Rule Learning." Machine Learning, 1997.](https://mlanthology.org/mlj/1997/furnkranz1997mlj-pruning/) doi:10.1023/A:1007329424533BibTeX
@article{furnkranz1997mlj-pruning,
title = {{Pruning Algorithms for Rule Learning}},
author = {Fürnkranz, Johannes},
journal = {Machine Learning},
year = {1997},
pages = {139-172},
doi = {10.1023/A:1007329424533},
volume = {27},
url = {https://mlanthology.org/mlj/1997/furnkranz1997mlj-pruning/}
}