Abstract -One of the main obstacles facing current fuzzy modelling techniques is that of dataset dimensionality. To enable these techniques to be effective, a redundancy-removing step is usually carried out heforehand. Rough Set Theory (RST) has been used as such a dataset pre-processor with much success, however it is reliant upon a crisp dataset; important information may he lost as a result of quantization. This paper proposes a new dimensionality reduction technique that.employs a hybrid variant of rough sets, fuzzy-mugh sets, to avoid this information loss.
A new dimensionality reduction technique that employs a hybrid variant of rough sets, fuzzy-rough sets, to avoid this information loss REF .
Fuzzy-rough sets for descriptive dimensionality reduction
2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291)
2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291)
Computer Science