Rule Induction: Combining Rough Set and Statistical Approaches
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.
KeywordsRough sets quality measures accuracy coverage significance rule induction rule selection
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