Abstract：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 sizeand data label.
王澍，李天瑞. 计算优势关系粗糙集中近似集的快速算法*[J]. 模式识别与人工智能, 2017, 30(2): 162-170.
WANG Shu, LI Tianrui. Fast Algorithm for Computing Approximations in Dominance-Based Rough Set. , 2017, 30(2): 162-170.
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