Double-Level Absolute Reduction for Multi-granulation Rough Sets
DENG Dayong1, HUANG Houkuan2
1.Xingzhi College, Zhejiang Normal University, Jinhua 321004 2.School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044
Abstract Multi-granulation rough set is a rough set model for heterogenous data in essence. However, it is still not employed to deal with heterogenous data. From the viewpoints of absolute attribute reduction, double-level absolute reduction for multi-granulation rough sets is proposed, including multi-granulation absolute recducts and multi-granulation absolute granulation reducts, and properties of double-level absolute reduction are analyzed from the perspective of heterogenous data. The algorithms for double-level absolute reduction are presented. Theoretical analysis and example show the validation of multi-granulation absolute reducts, multi-granulation absolute granulation reducts and double-level absolute reducts.
Fund:Supported by National Natural Science Foundation of China (No.61572442,61473030), Natural Science Foundation of Zhejiang Province (No.LY15F020012)
About author:: DENG DayongCorresponding author, born in 1968, Ph.D.,associate professor.His research interests include rough sets,granular computing and data mining. HUANG Houkuan, born in 1940, master, professor. His research interests include artificial intelligence, data mining and intelligent computing.
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2016, Vol. 29 
