An attribute reduction algorithm based on granular computing and discernibility

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.