Abstract：In traditional attribute reduction algorithms, all the data are loaded into the main memory once, which is hard to adapt to the big data analyses. Aiming at this problem, an attribute reduction algorithm based on granular computing and discernibility is proposed. An original large-scale datset is divided into small granularities by applying stratified sampling in statistics, and then attributes are reduced on each small granularity based on discernibility of attribute. Finally, all the reductions on small granularities are fused by weighting. Experimental results show that the proposed algorithm is feasible and efficient for attribute reduction on massive datasets.
冀素琴，石洪波，吕亚丽. 基于粒计算与区分能力的属性约简算法*[J]. 模式识别与人工智能, 2015, 28(4): 327-334.
JI Su-Qin, SHI Hong-Bo, Lü Ya-Li. An Attribute Reduction Algorithm Based on Granular Computing and Discernibility. , 2015, 28(4): 327-334.
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2015, Vol. 28 
