Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Predictive Performance of Weghted Relative Accuracy
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
On Meta-Learning Rule Learning Heuristics
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Journal of Intelligent Information Systems
BRACID: a comprehensive approach to learning rules from imbalanced data
Journal of Intelligent Information Systems
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In this paper, we argue that search heuristics for inductive rule learning algorithms typically trade off consistency and coverage, and we investigate this trade-off by determining optimal parameter settings for five different parametrized heuristics. This empirical comparison yields several interesting results. Of considerable practical importance are the default values that we establish for these heuristics, and for which we show that they outperform commonly used instantiations of these heuristics. We also gain some theoretical insights. For example, we note that it is important to relate the rule coverage to the class distribution, but that the true positive rate should be weighted more heavily than the false positive rate. We also find that the optimal parameter settings of these heuristics effectively implement quite similar preference criteria.