Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Case-based reasoning
Self-organizing maps
Rough set algorithms in classification problem
Rough set methods and applications
Comparing case-based reasoning classifiers for predicting high risk software components
Journal of Systems and Software
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
Foundations of Soft Case-Based Reasoning
Foundations of Soft Case-Based Reasoning
Applied Intelligence
Hi-index | 0.00 |
Case generation is a process of extracting representative cases to form a compact case base. In order 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 applied to identify the significant features. Different from the traditional discernibility function-based methods, the feature reduction method is based on a new concept of approximate reduct. The representative cases (prototypes) are then generated through LVQ learning process on the case bases after feature selection. LVQ is the supervised version of self-organizing map (SOM), which is more suitable to classification problems. Finally, a few of prototypes are generated as the representative cases of the original case base. These prototypes can be also considered as the extracted knowledge which improves the understanding of the case base. Three real life data are used in the experiments to demonstrate the effectiveness of this case generation approach. Several evaluation indices, such as classification accuracy, the storage space, case retrieval time and clustering performance in terms of intro-similarity and inter-similarity, are used in these testing.