Multi-granulation-based knowledge discovery in incomplete generalized multi-scale decision systems

被引：10
作者：
Wang, Jinbo [1 ,2 ]
Wu, Wei-Zhi [1 ,2 ]
Tan, Anhui [1 ,2 ]
机构：
[1] Zhejiang Ocean Univ, Sch Informat Engn, Zhoushan 316022, Zhejiang, Peoples R China
[2] Zhejiang Ocean Univ, Key Lab Oceanog Big Data Min & Applicat Zhejiang, Zhoushan 316022, Zhejiang, Peoples R China
来源：
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2022年 / 13卷 / 12期
基金：
中国国家自然科学基金;
关键词：
Belief functions; Granular computing; Incomplete decision systems; Multi-granulation rough sets; Multi-scale decision systems; OPTIMAL SCALE SELECTION; ROUGH SET APPROACH; ATTRIBUTE REDUCTION; ACQUISITION; FUSION; MODEL;
D O I：
10.1007/s13042-022-01634-3
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Multi-granulation rough sets and multi-scale data analysis are two active topics in granular computing. This study aims to investigate knowledge discovery in incomplete generalized multi-scale decision systems based on multi-granulation rough sets. Multi-granulation structures in incomplete generalized multi-scale decision systems are first discussed and updating mechanisms of information granules with the scale coarsening and refinement are described. Concepts of pessimistic upper and lower optimal scale combinations based on pessimistic multi-granulation rough sets are then defined and their uniqueness is verified. Evidence-theory-based numerical algorithms for finding optimal scale combinations are further designed. Notions of optimistic upper and lower optimal scale combinations based on optimistic multi-granulation rough sets are also introduced and their properties are examined. Finally, reducts of scale combinations are explored and an illustrative example is employed to elaborate multi-granulation rule acquisition approach in incomplete generalized multi-scale decision systems.
引用
收藏
页码：3963 / 3979
页数：17
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