多粒度粗糙集的双层绝对约简  

Double-Level Absolute Reduction for Multi-granulation Rough Sets

作　　者：邓大勇[1] 黄厚宽[2] 

机构地区：[1]浙江师范大学行知学院,金华321004 [2]北京交通大学计算机与信息技术学院,北京100044

出　　处：《模式识别与人工智能》2016年第11期969-975,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61572442;61473030);浙江省自然科学基金项目(No.LY15 F020012)资助~~

摘　　要：多粒度粗糙集本质上是异构的,但是目前尚未运用于异构数据处理.从绝对约简的角度出发,提出多粒度粗糙集的双层绝对约简——多粒度绝对约简和多粒度绝对粒度约简.分析多粒度双层绝对约简的性质,特别是从异构数据约简的角度探究多粒度双层绝对约简的特性,提出多粒度双层绝对约简算法.理论分析和实例表明多粒度双层绝对约简算法的可行性.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.

关 键 词：粗糙集 多粒度粗糙集 异构数据 绝对约简 双层绝对约简 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]