C4.5: programs for machine learning
C4.5: programs for machine learning
Unifying instance-based and rule-based induction
Machine Learning
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Robust Classification for Imprecise Environments
Machine Learning
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Tree Induction for Probability-Based Ranking
Machine Learning
Appendix B: information-theoretic tree and rule induction
Intelligent data analysis
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
PRIE: a system for generating rulelists to maximize ROC performance
Data Mining and Knowledge Discovery
Correlated itemset mining in ROC space: a constraint programming approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploring multi-objective PSO and GRASP-PR for rule induction
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Reinventing machine learning with ROC analysis
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
An integrated approach to learning bayesian networks of rules
ECML'05 Proceedings of the 16th European conference on Machine Learning
Strategies to parallelize ILP systems
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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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.