Attribute Reduction of Neighborhood Rough Set Based on Discernment

Abstract

For neighborhood rough set attribute reduction algorithms based on dependency degree, a neighborhood computation method incorporating attribute weight values and a neighborhood rough set attribute reduction algorithm using discernment as the heuristic information was proposed. The reduction algorithm comprehensively considers the dependency degree and neighborhood granulation degree of attributes, allowing for a more accurate measurement of the importance degrees of attributes. Example analyses and experimental results demonstrate the feasibility and effectiveness of the algorithm.