中文摘要：

基于粒计算视角，提出粒化一融合框架下的海量高维数据特征选择算法．运用BLB（Bag of Little Bootstrap）的思想，首先将原始海量数据集粒化为小规模数据子集（粒），然后在每个粒上构建多个自助子集的套索模型，实现粒特征选择，最后，各粒特征选择结果按权重融合、排序，得到原始数据集的有序特征选择结果．人工数据集和真实数据集上的实验表明文中算法对海量高维数据集进行特征选择的可行性和有效性．

英文摘要：

From a granular computing perspective, a feature selection algorithm based on granutatlon~tuslon Ior 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.