Abstract 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.
About author:: JI Suqin (Corresponding author), born in 1972, master, lecturer. Her research interests include data mining and distributed technology.SHI Hongbo, born in 1965, Ph.D., professor. Her research interests include machine learning and data mining.吕亚丽,女,1975年生,博士,副教授,主要研究方向为人工智能、数据挖掘.E-mail:yali.lv2008@gmail.com. L Yali, born in 1975, Ph.D., associate professor. Her research interests include artificial intelligence and data mining.GUO Min, born in 1978, Ph.D. candidate, lecturer. Her research interests include applied statistics.
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2016, Vol. 29 
