Comparison of rough-set and statistical methods in inductive learning
International Journal of Man-Machine Studies
Variable precision rough set model
Journal of Computer and System Sciences
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets, Fuzzy Sets and Knowledge Discovery
Rough Sets, Fuzzy Sets and Knowledge Discovery
Mining Knowledge Rules from Databases: A Rough Set Approach
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
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Classification rules induction is a central problem addressed by machine learning and data mining. Rough sets theory is an important tool for data classification. Traditional rough sets approach, however, pursuits the fully correct or certain classification rules without considering other factors such as uncertain class labeling, importance of examples, as well as the uncertainty of the final rules. A generalized rough sets model, GRS, is proposed and a classification rules induction approach based on GRS is suggested. Our approach extends the variable precision rough sets model and attempts to reduce the influence of noise by considering the importance of each training example and handling the uncertain class labels. The final classification rules are also measured with the uncertainty factor.