Information-Based Evaluation Criterion for Classifier's Performance
Machine Learning
An Empirical Study on Rule Quality Measures
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
ELEM2: A Learning System for More Accurate Classifications
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Controlled Redundancy in Incremental Rule Learning
ECML '93 Proceedings of the European Conference on Machine Learning
Concept learning and the problem of small disjuncts
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
A Case Study for Learning from Imbalanced Data Sets
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
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Rule quality measures can help to determine when to stop generalization or specification of rules in a rule induction system. Rule quality measures can also help to resolve conflicts among rules in a rule classification system. We enlarge our previous set of statistical and empirical rule quality formulas which we tested earlier on a number of standard machine learning data sets. We describe this new set of formulas, performing extensive tests which also go beyond our earlier tests, to compare these formulas. We also specify how to generate formula-behavior rules from our experimental results, which show the relationships between a formula's performance and the characteristics of a dataset. Formula-behavior rules can be combined into formula-selection rules which can select a rule quality formula before rule induction takes place. We report the experimental results showing the effects of formula-selection on the predictive performance of a rule induction system.