Rough set is one of the important methods for rule acquisition and attribute reduction. The current goal of rough set attribute reduction focuses more on minimizing the number of reduced attributes, but ignores the spatial similarity between reduced and decision attributes, which may lead to problems such as increased number of rules and limited generality. In this paper, a rough set attribute reduction algorithm based on spatial optimization is proposed. By introducing the concept of spatial similarity, to find the reduction with the highest spatial similarity, so that the spatial similarity between reduction and decision attributes is higher, and more concise and widespread rules are obtained. In addition, a comparative experiment with the traditional rough set attribute reduction algorithms is designed to prove the effectiveness of the rough set attribute reduction algorithm based on spatial optimization, which has made significant improvements on many datasets.

提出了一种基于空间优化的粗糙集属性约简算法，通过引入空间相似性的概念，找到具有最高空间相似性的约简，使得约简与决策属性之间的空间相似性更高，从而得到更加简明和广泛的规则，并通过与传统的粗糙集属性约简算法的比较实验证明了基于空间优化的粗糙集属性约简算法的有效性，对许多数据集取得了显著改进。