基于相对知识粒度的区间集决策信息表不确定性度量

Uncertainty measurement for interval-set decision information tables based on relative knowledge granularity

区间集决策信息表拓展了经典决策信息表,其粒化结构的不确定性刻画成为重要应用基础。基于区间邻域粒化引入相对知识粒度,进而提出区间集决策信息表的一种新型不确定性度量。利用两对象之间的距离及半径来建立区间邻域粒化系统,证明距离粒化与相似度刻画的条件等价性,获得区间近似粗糙度;把经典相对知识粒度推广为区间相对知识粒度,将其与区间近似粗糙度进行信息融合,提出一种新型不确定性度量并得到了粒化单调性等性质。最后进行了实例验证,结果表明所提度量能够有效表征粒化结构变化所引起的不确定性变化。

Interval-set decision information tables extend classic decision information tables,and its uncertainty characterization based on its granulation structure becomes an important application basis.The relative knowledge granularity is introduced by the interval neighborhood granulation,and then a new uncertainty measurement of interval-set decision information tables is proposed.Firstly,the interval neighborhood granulation system is established by using the distance and radius between two objects.The conditional equivalence between the distance granulation and similarity characterization is proved,and the interval approximate roughness is obtained.Then,the interval relative knowledge granularity is constructed from the classic relative knowledge granularity,and it is further fused with the interval approximate roughness.Thus,a new uncertainty measurement emerges to have good properties such as the granulation monotonicity.Finally,an example is given for verification and the results show that the proposed measurement can effectively represent the uncertainty caused by the change of granulation structure.