Improved feature selection algorithm with fuzzy-rough sets on compact computational domain

TLDR: An efficient input feature selection algorithm for modeling of systems based on modified definition of fuzzy-rough sets based on some natural properties of fuzzy t-norm and t-conorm operators is put forward, which is then utilized to construct improved Fuzzy-Rough Feature Selection algorithm.

Abstract: The aim of this paper is to provide an efficient input feature selection algorithm for modeling of systems based on modified definition of fuzzy-rough sets. Some of the critical issues concerning the complexity and convergence of the feature selection algorithm are discussed in detail. Based on some natural properties of fuzzy t-norm and t-conorm operators, the concept of fuzzy-rough sets on compact computational domain is put forward, which is then utilized to construct improved Fuzzy-Rough Feature Selection algorithm. Various mathematical properties of this new definition of fuzzy-rough sets are discussed from pattern classification viewpoint. Speedup factor as high as 622 has been achieved with proposed algorithm compared to recently proposed FRSAR, with improved model performance on selected set of features.