ROCCER: An Algorithm for Rule Learning Based on ROC Analysis
Abstract
We introduce a rule selection algorithm called ROCCER, which operates by selecting classification rules from a larger set of rules ? for instance found by Apriori ? using ROC analysis. Experimental comparison with rule induction algorithms shows that ROCCER tends to produce considerably smaller rule sets with compatible Area Under the ROC Curve (AUC) values. The individual rules that compose the rule set also have higher support and stronger association indexes.
Cite
Text
Prati and Flach. "ROCCER: An Algorithm for Rule Learning Based on ROC Analysis." International Joint Conference on Artificial Intelligence, 2005.Markdown
[Prati and Flach. "ROCCER: An Algorithm for Rule Learning Based on ROC Analysis." International Joint Conference on Artificial Intelligence, 2005.](https://mlanthology.org/ijcai/2005/prati2005ijcai-roccer/)BibTeX
@inproceedings{prati2005ijcai-roccer,
title = {{ROCCER: An Algorithm for Rule Learning Based on ROC Analysis}},
author = {Prati, Ronaldo C. and Flach, Peter A.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2005},
pages = {823-828},
url = {https://mlanthology.org/ijcai/2005/prati2005ijcai-roccer/}
}