串行概率粗糙集近似

Serial Probabilistic Rough Set Approximations

经典的概率粗糙集模型是基于等价关系和条件概率提出的。但在实际应用中,知识库存在多种不确定性因素,使得对象间的关系未必满足等价关系。因此在保证条件概率有意义的情况下,将等价关系推广到串行二元关系,讨论了串行关系下的概率粗糙集近似;研究了当目标概念发生变化时,串行概率粗糙下、上近似的性质;进一步,通过调整两个阈值,给出了对应的串行概率粗糙下、上近似的变化趋势。

The classical probabilistic rough set model was proposed based on an equivalence relation and a conditional probability.However,uncertainty in knowledge base makes it difficult to satisfy the equivalence relation between any two objects.This paper considered the serial binary relation instead of an equivalence relation,making the conditional probability meaningful.Then the serial probabilistic rough set approximations were introduced based on a serial relation.Properties of the serial probabilistic rough lower and upper approximations were discussed when the target concepts are variable.Furthermore,by adjusting the two thresholds,the corresponding serial probabilistic rough lower and upper approximations were also investigated.