Learning complicated concepts reliably and usefully
COLT '88 Proceedings of the first annual workshop on Computational learning theory
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Robust Classification for Imprecise Environments
Machine Learning
Machine Learning
Descriptive Induction through Subgroup Discovery: A Case Study in a Medical Domain
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
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
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Coronary Heart Disease Patient Models Based on Inductive Machine Learning
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Making CN2-SD subgroup discovery algorithm scalable to large size data sets using instance selection
Expert Systems with Applications: An International Journal
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This paper discusses actionable knowledge generation. Actionable knowledge is explicit symbolic knowledge, typically presented in the form of rules, that allows the decision maker to recognize some important relations and to perform an action, such as targeting a direct marketing campaign, or planning a population screening campaign aimed at targeting individuals with high disease risk. The disadvantages of using standard classification rule learning for this task are discussed, and a subgroup discovery approach proposed. This approach uses a novel definition of rule quality which is extensively discussed.