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.
| Translated title of the contribution | ROCCER: an Algorithm for Rule Learning Based on ROC Analysis |
|---|---|
| Original language | English |
| Title of host publication | Unknown |
| Publisher | IJCAI |
| Pages | 823 - 828 |
| Number of pages | 5 |
| ISBN (Print) | 0938075934 |
| Publication status | Published - Aug 2005 |