Advances in the Dempster-Shafer theory of evidence
Communications of the ACM
Using neural networks for data mining
Future Generation Computer Systems - Special double issue on data mining
Software Engineering: An Engineering Approach
Software Engineering: An Engineering Approach
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Towards Rough Neural Computing Based on Rough Membership Functions: Theory and Application
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Introduction to the special issue on neural networks for data mining and knowledge discovery
IEEE Transactions on Neural Networks
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Classification is an important theme in data mining. Rough sets and neural networks are two technologies frequently applied to data mining tasks. Integrating the advantages of two approaches, this paper presents a hybrid system to extract efficiently classification rules from a decision table. The neural network system and rough set theory are completely integrated to into a hybrid system and use cooperatively for classification support. Through rough set approach a decision table is first reduced by removing redundant attributes without any classification information loss. Then a rough neural network is trained to extract the rules set form the reduced decision table. Finally, classification rules are generated from the reduced decision table by rough neural network. In addition, a new algorithm of finding a reduct and a new algorithm of rule generation from a decision table are also proposed. The effectiveness of our approach is verified by the experiments comparing with traditional rough set approach.