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.
Fund:Supported by National Natural Science Foundation of China(No.61573292)
About author:: (WANG Shu, born in 1978, Ph.D. candidate. His research interests include rough set, granular computing and big data.)(LI Tianrui(Corresponding author), born in 1969, Ph.D., professor. His research interests include big data, cloud computing, rough set and granular computing.)
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2017, Vol. 30 
