ROCCER: an Algorithm for Rule Learning Based on ROC Analysis

RC Prati, PA Flach

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

25 Citations (Scopus)

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 contributionROCCER: an Algorithm for Rule Learning Based on ROC Analysis
Original languageEnglish
Title of host publicationUnknown
PublisherIJCAI
Pages823 - 828
Number of pages5
ISBN (Print)0938075934
Publication statusPublished - Aug 2005

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI'05)

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