Attribute reduction based on neighborhood constrained fuzzy rough sets

Abstract

The construction of fuzzy relations is a key issue of fuzzy rough sets. The fuzzy relations generated by the soft distances between samples are more robust than that generated by the hard distances between samples. To improve the ability of fuzzy rough sets in deleting redundant attributes, we propose two enhanced fuzzy similarity relations by fully mining neighborhood information and decision information of samples. Then, we establish the Neighborhood Constrained Fuzzy Rough Sets (NC-FRS) by using the proposed relations to perform attribute reduction. Meanwhile, we design enhanced fuzzy similarity relation-based attribute reduction (EFSR-AR) to select important attributes for classification tasks. Finally, we download three gene expression profiles from NCBI to verify that the proposed algorithm can select genes highly related to tumors, the selected genes are more conducive to tumor classification, and the proposed algorithm has strong anti-noise ability. The comparison results indicate that EFSR-AR does have the ability to combat noise and select some genes highly related to tumors.