Attribute reduction based on optimized granular ball neighborhood rough sets

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Abstract

Neighborhood rough sets (NRS) are commonly applied to attribute reduction because they can process numerical data directly without discretization. Granular ball neighborhood rough sets extend NRS by adaptively determining neighborhood radii according to the data distribution. However, existing granular ball generation and reduction algorithms can be further enhanced in terms of granule representation, radius computation, and attribute evaluation. To address these issues, an optimized granular ball neighborhood rough set model (OGNRS) and a corresponding attribute reduction algorithm are proposed. In this model, a scoring function integrating density and centroid-based distance is employed to select centers, and a median absolute deviation-based strategy is introduced to determine the radii of granular balls. In addition, the minimal-redundancy-maximal-relevance criterion is incorporated into the reduction process to provide search guidance and reduce redundancy among selected attributes. Experiments on 14 UCI datasets demonstrate that OGNRS achieves competitive overall performance in terms of classification accuracy, reduct size, and computational efficiency.

Keywords

rough set / granular computing / granular ball / attribute reduction

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