New Developments of FRFS

Fuzzy-rough set-based feature selection has been shown to be highly useful at reducing data dimensionality, but possesses several problems that render it ineffective for datasets possessing tens of thousands of features. This chapter presents three new approaches to fuzzy-rough feature selection based on fuzzy similarity relations. The first employs the new similarity-based fuzzy lower approximation to locate subsets. The second uses boundary region information to guide search. Finally, a fuzzy extension to crisp discernibility matrices is given in order to discover fuzzy-rough subsets. The methods are evaluated and compared using benchmark data.

The latest developments can be found here.

R. Jensen and Q. Shen. Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches. IEEE Press/Wiley & Sons.