邻域系统的不确定性度量方法

针对离散型数据系统的不确定性度量方法难以有效解决邻域系统不确定性度量的问题,引入邻域粗糙集模型,提出邻域精确度、邻域知识粒度和基于邻域知识粒度的近似精度等邻域系统不确定性度量方法,进一步从理论上证明其有效性.实验结果表明,基于邻域知识粒度的近似精度具有更严格的单调性,优于邻域近似精度的邻域系统对不确定性度量的效果.

Neighborhood system uncertainty measurement approaches

Uncertainty measures for the discrete data system can not be effective for the measurement of uncertainty ofneighborhood system. Therefore, the neighborhood rough set model is introduced to propose measure approaches such as neighborhood accuracy, neighborhood knowledge granularity as well as neighborhood approximation accuracy based on knowledge granularity. The effectiveness of the approaches are verified theoretically. Experimental results show that approximation accuracy based on knowledge granularity with stricter monotonicity outperforms the neighborhood approximation accuracy for the uncertainty measurement of the neighborhood system.