全粒度粗糙集的不确定性

全粒度粗糙集是一种既能表示显式知识又能表示隐式知识的粗糙集模型, 能更好地表示人类认识的复杂性、多样性和不确定性.文中结合经典粗糙集理论,定义全粒度隶属度、全粒度粗糙度、概念的全粒度属性依赖度、决策系统的全粒度属性依赖度等不确定性指标,探究这些不确定性指标的性质,指出这些不确定指标与全粒度绝对约简、概念的全粒度属性约简、全粒度Pawlak约简的联系,有助于全粒度粗糙集的属性约简和实际应用.

Uncertainty of Entire-Granulation Rough Sets

Entire-granulation rough sets can express explicit and implicit knowledge, as well as complexity, diversity and uncertainty of human cognition. Combined with classic rough set theory, several uncertainty indexes in entire-granulation rough sets are defined, including membership degree of entire-granulation, roughness degree of entire-granulation, dependence degree of entire-granulation for a single concept and dependence degree of entire-granulation for a decision system. The properties of these indexes are investigated, and the relations between these indexes and absolute attribute reducts of entire-granulation, attribute reducts of entire-granulation for a single concept and entire-granulation Pawlak reducts are indicated. The result is a theoretical foundation for attribute reduction and practical application of entire- granulation rough sets.