计算优势关系粗糙集中近似集的快速算法

Fast Algorithm for Computing Approximations in Dominance-Based Rough Set

优势关系粗糙集克服经典粗糙集无法处理偏序关系数据的缺陷,而减少近似集的计算时间可以提高数据处理的效率.基于此种情况,文中提出计算优势关系粗糙集中近似集的快速算法,在对象和属性同时增加时,能快速计算优势关系粗糙集的近似集.算法改进近似集相关参数的定义,通过尽可能少的参数求出近似集,简化计算过程,提高算法运算速度,节省内存.实验表明,文中算法具有较快的运算速度,尤其当数据量增大或数据类别增多时效果更明显.

The classical rough set can not process preference-ordered data. Dominance-based rough set （DRST） overcomes this drawback. The data processing efficiency can be improved by reducing the time of computing approximations. A fast algorithm for computing approximations is presented. The approximations are acquired quickly while objects and attributes being added simultaneously in DRST. The definitions of parameters related to approximations are revised in the proposed fast algorithm and thus approximations can be calculated by parameters as few as possible. Consequently, the calculation is simplified and accelerated, and the memory consumption is reduced as well. The experimental results demonstrate that the proposed algorithm is faster than other algorithms and it is especially efficient with larger data size and data label.