Using the m-estimate in rule induction
Journal of Computing and Information Technology
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Biochemical Knowledge Discovery Using Inductive Logic Programming
DS '98 Proceedings of the First International Conference on Discovery Science
Generating Actionable Knowledge by Expert-Guided Subgroup Discovery
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
DS '08 Proceedings of the 11th International Conference on Discovery Science
A rule-based method for customer churn prediction in telecommunication services
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Why is rule learning optimistic and how to correct it
ECML'06 Proceedings of the 17th European conference on Machine Learning
Local patterns: theory and practice of constraint-based relational subgroup discovery
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Inductive querying for discovering subgroups and clusters
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Active subgroup mining: a case study in coronary heart disease risk group detection
Artificial Intelligence in Medicine
Heuristic rule-based regression via dynamic reduction to classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Simulation knowledge extraction and reuse in constrained random processor verification
Proceedings of the 50th Annual Design Automation Conference
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Weighted relative accuracy was proposed in [4] as an alternative to classification accuracy typically used in inductive rule learners. Weighted relative accuracy takes into account the improvement of the accuracy relative to the default rule (i.e., the rule stating that the same class should be assigned to all examples), and also explicitly incorporates the generality of a rule (i.e., the number of examples covered). In order to measure the predictive performance of weighted relative accuracy, we implemented it in the rule induction algorithm CN2. Our main results are that weighted relative accuracy dramatically reduces the size of the rule sets induced with CN2 (on average by a factor 9 on the 23 datasets we used), at the expense of only a small average drop in classification accuracy.