Feature selection algorithm based on granulation-fusion for massive high-dimension data

From a granular computing perspective, a feature selection algorithm based on granulation-fusion for massive and high-dimension data is proposed. By applying bag of little Bootstrap (BLB), the original massive dataset is granulated into small subsets of data (granularity), and then features are selected by constructing multiple least absolute shrinkage and selection operator (LASSO) models on each granularity. Finally, features selected on each granularity are fused with different weights, and feature selection results are obtained on original dataset through ordering. Experimental results on artificial datasets and real datasets show that the proposed algorithm is feasible and effective for massive high-dimension datasets.