As fundamental abstract constructs supporting the human-centered way of Granular Computing (GrC), information granules can be used to distinguish different classes of data from the perspective of easily understood geometrical structure. In this study, a three-stage rule-based granular classification method is proposed using a union of a series of hypersphere information granules. The first stage focuses on dividing each class of data into a series of chunks. The second stage concerns the construction of some hyperspheres around these chunks. These resulting hyperspheres form a union information granule to depict the key structural characteristics of the corresponding data through their union operation. At the final stage, the union information granules are refined and the rule-based granular classification model is emerged through using a series of “If-Then” rules to articulate the refined union information granule formed for each class with the corresponding class label. A number of experiments involving several synthetic and publicly available datasets are implemented to exhibit the advantages of the resulting classifier. The impacts of critical parameters on the performance of the constructed classifier are also revealed.

作为支持以人为中心的粒度计算（GrC）方式的基本抽象结构，信息粒度可用于从易于理解的几何结构的角度区分不同类别的数据。在这项研究中，提出了一种使用一系列超球体信息粒子的并集的基于三阶段规则的粒度分类方法。第一阶段着重于将每类数据划分为一系列块。第二阶段涉及围绕这些块构建一些超球体。这些产生的超球形成联合信息颗粒，以通过其联合操作来描述相应数据的关键结构特征。在最后阶段 通过使用一系列“ If-Then”规则对每个类形成的带有相应类标签的精制工会信息颗粒进行清晰表达，从而对工会信息颗粒进行细化，并建立基于规则的粒度分类模型。进行了一些涉及几个合成的和公开可用的数据集的实验，以展示所得分类器的优势。还揭示了关键参数对构造的分类器性能的影响。