基于近似精度和条件信息熵的粗糙集不确定性度量方法

基于粗糙集理论的基本不确定性度量方法能够度量知识的不确定大小，却不能区分集合粒度划分大小，利用知识的信息熵，并结合粗糙集精度，定义了一种粗糙熵，用于度量粗糙集中的不确定性．证明了该粗糙熵随着划分粒度的变小而单调增加的性质．新的粗糙熵不仅能度量粗糙集的不确定区域大小而且可以度量划分的大小．算例证明了该方法的有效性．

A New Method for Measuring Uncertainty in Rough Sets

There exist uncertainties in rough set, and how to measure these uncertainties is a valuable problem to study. The basic methods for measuring uncertainty in rough set can not distinguish different granularity of partitions. According to the definitions of accuracy of rough set and information entropy, this paper presented a new method for measuring uncertainty in rough set, and proved that this rough entropy monotonously increases while the partition granularity decreases. The new rough entropy can measure not only the size of uncertainty region in rough set but also the partition granularity. A practical example shows that this new method is effective.