Fuzzy-rough sets for descriptive dimensionality reduction
Richard Jensen, Qiang Shen · 2003
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 beforehand. 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 be lost as a result of quantization. The paper proposes a dimensionality reduction technique that employs a hybrid variant of rough sets, fuzzy-rough sets, to avoid this information loss.