摘要
经典的Pawlak概率粗糙集模型是基于论域上的等价关系而建立的,然而在实际应用中等价关系很难得到。因此,许多学者建立了基于一般关系(如容差关系、相似关系等)的Pawlak粗糙集模型。本文建立了基于覆盖关系的概率粗糙集模型,推广和总结了前人的工作。同时,提出了该模型下的Bayes决策方法和应用实例。
The classical probabilistic rough sets are defined based on the equivalent relations of the universe. However, the equivalent relation of the universe is often difficult to obtain, furthermore, it has some restrictions in the real applications. This paper devote to the study of the covering probabilistic rough set models in order to solve the problems proposed above： the covering probabilistic rough set models are presented, and as an application, a Bayes decision procedure in medical diagnosis is discussed.
出处
《模糊系统与数学》
CSCD
北大核心
2008年第4期142-148,共7页
Fuzzy Systems and Mathematics
基金
国家自然科学基金资助项目(10771171)
甘肃省教育厅科研基金资助项目(0601-20)
