摘要： 在粗糙集理论中，上、下近似集的计算是核心问题。矩阵能提供一种高效的方法来计算粗糙集模型中概念的上、下近似集，但是在目前的矩阵方法中，论域中每个对象都要与论域中全部对象进行运算，从而导致较大的时间代价。为提高使用矩阵计算近似集时的效率，提出一种快速计算上、下近似集的矩阵方法。对于一个给定的概念，基于概念的外延和概念补集的外延构建一个局部关系矩阵，对局部关系矩阵和单位矩阵进行矩阵运算得到正域和边界域布尔矩阵，对局部关系矩阵的转置和单位矩阵进行矩阵运算得到负域和边界域布尔矩阵。在该矩阵方法中，只需要根据局部关系矩阵就可以计算近似集，即论域中每个对象不必和论域中全部对象进行运算就可以被划分到相应的区域，使得算法的运算次数相比传统矩阵算法大幅减少，从而降低时间成本。在8个公开数据集上的实验结果表明，与4种传统的矩阵算法相比，该矩阵算法的运行速度至少提升70%，可以有效提高近似集计算效率。

关键词: 粗糙集, 近似集, 单位矩阵, 局部关系矩阵, 矩阵运算

Abstract: The calculation of upper and lower approximations is the core issue in rough set theory. Matrices can provide an efficient method for calculating the upper and lower approximations of concepts in rough set models. However，in current matrix methods，each object in the universe must be operated on with all objects in the universe， resulting in a significant time cost.To improve the efficiency of calculating approximations using matrices，a matrix method is proposed for quickly calculating upper and lower approximations. For a given concept，a local relational matrix is constructed based on the extensions of the concept and its complement. Matrix operations are performed on the local relational and identity matrices to obtain positive and boundary domain Boolean matrices. Subsequently，matrix operations are performed on the transposition of the local relational and identity matrices to obtain negative and boundary domain Boolean matrices. In this matrix method，only calculating the approximation based on the local relational matrix is necessary，that is，each object in the universe can be divided into corresponding regions without needing to perform operations on all objects in the universe，which significantly reduces the number of operations of the algorithm compared with traditional matrix algorithms，thereby reducing the time cost. Experiments on eight open datasets show that，compared with four traditional matrix algorithms，the proposed matrix algorithm can improve the running speed by at least 70% and effectively improve the efficiency of the approximation calculation.

Key words: rough set, approximation, identity matrix, local relation matrix, matrix operation

中图分类号: 

• TP391