基于可变精度的不完备信息系统粗集扩展模型及属性约简

在变精度的基础上,定义了变精度的重要性算子和变精度的近似约简等概念,并由此给出了一种基于变精度的属性约简算法.算法既能保证属性约简的准确性,又能通过对相似度α和精度β的调节增加其灵活性,从而得到一个满足相似度和精度要求的近似约简.同时,它也是完备信息系统的属性约简算法的推广(当α=1,β=0时).且通过实例分析说明算法的可行性和有效性.

Extension of rough set model based on variable precision under incomplete information systems and attribute reduction

Attribute reduction is one of the key problems in rough set theory.By introducing some concepts such as significance operator of attributes and approximate reduction,a new algorithm for attribute reduction based on variable precision is proposed in this paper.Not only has the accuracy of attribute reduction be enguaranteed,but also its flexibility has been increased by adjusting the values of the similarity α and the relatively classified faultrate β,and thus the approximate reduction satisfing the requirements of similarity and precision has been obtained.In addition,this algorithm is the generalization of the algorithm in complete information system.Finally,an example shows that this new algorithm is both feasible and effective in practice.