Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Rough sets: probabilistic versus deterministic approach
International Journal of Man-Machine Studies
Classifier systems and genetic algorithms
Machine learning: paradigms and methods
A decision-theoretic roguth set model
Methodologies for intelligent systems, 5
A decision theoretic framework for approximating concepts
International Journal of Man-Machine Studies
Variable precision rough set model
Journal of Computer and System Sciences
A new version of the rule induction system LERS
Fundamenta Informaticae
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Optimal Decision Making with Data-Acquired Decision Tables
Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
Data mining based on rough sets
Data mining
Decision-theoretic rough set models
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
An empirical comparison of rule sets induced by LERS and probabilistic rough classification
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
| Hi-index | 0.01 |
The LERS classification system and rule management in probabilistic rough set models (PRSM) are compared according to the interpretations of rules, quantitative measures of rules, and rule conflict resolution when applying rules to classify new cases. Based on the notions of positive and boundary regions, probabilistic rules are semantically interpreted as the positive and boundary rules, respectively. Rules are associated with different quantitative measures in LERS and PRSM, reflecting different characteristics of rules. Finally, the rule conflict resolution method used in LERS may be applied to PRSM.