Knowledge reduction of dynamic covering decision information systems caused by variations of attribute values

被引:19
作者
Cai, Mingjie [1 ,2 ]
Li, Qingguo [1 ]
Ma, Jianmin [3 ]
机构
[1] Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China
[2] Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada
[3] Changan Univ, Dept Math & Informat Sci, Fac Sci, Xian 710064, Shaanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Boolean matrix; Characteristic matrix; Dynamic covering approximation space; Dynamic Covering decision information system; Rough set; INCREMENTAL UPDATING APPROXIMATIONS; ROUGH SETS; MAINTENANCE; MATRIX; ALGORITHMS; FUZZINESS;
D O I
10.1007/s13042-015-0484-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In practical situations, it is time-consuming to conduct knowledge reduction of dynamic covering decision information systems caused by variations of attribute values with the non-incremental approaches. In this paper, motivated by the need for knowledge reduction of dynamic covering decision information systems, we introduce incremental approaches to computing the type-1 and type-2 characteristic matrices for constructing the second and sixth lower and upper approximations of sets in dynamic covering approximation spaces caused by revising attribute attributes. We also employ several examples to explain how to compute the second and sixth lower and upper approximations of sets in dynamic covering approximation spaces. Then we propose the incremental algorithms for computing the second and sixth lower and upper approximations of sets and employ experimental results to illustrate the incremental algorithms are effective to calculate the second and sixth lower and upper approximations of sets in dynamic covering approximation spaces. Finally, we give two examples to show how to conduct knowledge reduction of dynamic covering decision information systems caused by altering attribute values.
引用
收藏
页码:1131 / 1144
页数:14
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