Rule Combination in Inductive Learning
Abstract
This paper describes the work on methods for combining rules obtained by machine learning systems. Three methods for obtaining the classification of examples with those rules are compared. The advantages and disadvantages of each method are discussed and the results obtained on three real world domains are commented. The methods compared are: selection of the best rule; PROSPECTOR-like probabilistic approximation for rule combination; and MYCIN-like approximation. Results show significant differences between methods indicating that the problem-solving strategy is important for accuracy oflearning systems.
Cite
Text
Torgo. "Rule Combination in Inductive Learning." European Conference on Machine Learning, 1993. doi:10.1007/3-540-56602-3_155Markdown
[Torgo. "Rule Combination in Inductive Learning." European Conference on Machine Learning, 1993.](https://mlanthology.org/ecmlpkdd/1993/torgo1993ecml-rule/) doi:10.1007/3-540-56602-3_155BibTeX
@inproceedings{torgo1993ecml-rule,
title = {{Rule Combination in Inductive Learning}},
author = {Torgo, Luís},
booktitle = {European Conference on Machine Learning},
year = {1993},
pages = {384-389},
doi = {10.1007/3-540-56602-3_155},
url = {https://mlanthology.org/ecmlpkdd/1993/torgo1993ecml-rule/}
}