一种悲观多粒度粗糙集中的粒度约简算法

A Granular Space Reduction Approach to Pessimistic Multi-Granulation Rough Sets

多粒度粗糙集方法是近年来粗糙集理论的一个发展方向,它是一种基于多个粒空间的粗糙数据建模方法.文中针对悲观多粒度粗糙集模型,引入分布约简的概念,分析多个粒空间中的粒度选择问题.基于给出的粒度重要度提出悲观多粒度粗糙集中的粒度约简算法,并通过实例验证该方法的有效性.结论表明该方法得到的结果更加符合实际决策.

Multi-granulation rough set method （MGRS） is one of new directions in rough set theory. It is a data modeling method in the context of multiple granular spaces. Firstly, a concept of distribution reduction is introduced to pessimistic multi-granulation rough model, and a granular space selection under multiple granular spaces is investigated. Then, the important measure of a granular space in this model is defined, and an algorithm is designed to obtain a granular space reduction in the pessimistic multi-granulation rough model. Finally, an example is employed to verify the validity of the proposed algorithm. The obtained results are much closer to the practical decision.