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.

翻译：粗糙集是规则获取与属性约简的重要方法之一。当前粗糙集属性约简的目标更侧重于减少约简属性的数量，但忽略了约简属性与决策属性之间的空间相似性，这可能导致规则数量增加、泛化能力受限等问题。本文提出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概念，寻找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，从而获得更简洁且更具普适性的规则。此外，设计了与传统粗糙集属性约简算法的对比实验，验证了基于空间优化的粗糙集属性约简算法的有效性，该算法在多个数据集上取得了显著改进。