A granular-ball generation method based on local density for classification

Abstract

As a new branch of granular computing, granular-ball computing (GBC) has become increasingly popular due to its high efficiency, robustness, and scalability. However, in classification tasks, the existing mainstream methods for generating granular-ball (GB) have two common issues: GBs generated by the existing methods are not accurate enough to describe the distribution of the original data and there are de-overlap operations in the existing GB generation process, which increase the workload of the GB generation process. Therefore, to solve the above two issues, a GB generation method based on local density (LDGBG) is proposed in this paper. First, centers of GBs are selected based on local density to ensure the generated GBs are more consistent with the original data distribution. Second, the method for calculating radii avoids overlaps and incorporates the idea of compact within class and decentralized between classes, which will improve classification performance. Furthermore, a sparsity index is introduced to assess the sparsity of datasets, thereby enabling more effective utilization of original samples in sparse datasets. Finally, comparative experiments are conducted on 27 benchmark datasets and the experimental results show that LDGBG is superior to the existing mainstream models in effectiveness and robustness.