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Multi-granulation-based knowledge discovery in incomplete generalized multi-scale decision systems
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DOI：10.1007/s13042-022-01634-3.png)
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摘要
En 中文
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.
Keyword:
Belief functions
Granular computing
Incomplete decision systems
Multi-granulation rough sets
Multi-scale decision systems
期刊
IF:
2.7
论文数:
3.2K
被引数:
5.6K
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