Case-based reasoning
Self-organizing maps
Rough set algorithms in classification problem
Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Reduction algorithms based on discernibility matrix: the ordered attributes method
Journal of Computer Science and Technology
Boolean Reasoning for Feature Extraction Problems
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
Applied Intelligence
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To build competent and efficient CBR classifiers, we develop a case generation approach which integrates fuzzy sets, rough sets and learning vector quantization (LVQ). If the feature values of the cases are numerical, fuzzy sets are firstly used to discretize the feature spaces. Secondly, a fast rough set-based feature selection method is built to identify the significant features. The representative cases (prototypes) are then generated through LVQ learning process on the case bases after feature selection. These prototypes can be also considered as the extracted knowledge which improves the understanding of the case base. Three real life data sets are used in the experiments to demonstrate the effectiveness of this case generation approach.