Statistical techniques for rough set data analysis
Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Accuracy and Coverage in Rough Set Rule Induction
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Fundamenta Informaticae
Quality Measures in Data Mining (Studies in Computational Intelligence)
Quality Measures in Data Mining (Studies in Computational Intelligence)
Model selection and assessment for classification using validation
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Approximation spaces and information granulation
Transactions on Rough Sets III
A new rule induction method from a decision table using a statistical test
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
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In this paper we propose the hybridisation of the rough set concepts and statistical learning theory. We introduce new estimators for rule accuracy and coverage, which base on the assumptions of the statistical learning theory. Then we construct classifier which uses these estimators for rule induction. These estimators allow us to select rules describing statistically significant dependencies in data. We test our classifier on benchmark datasets and show its applications for KDD.