相斥邻域的覆盖粗糙集实值属性约简

粗糙集理论是一种有效的属性约简方法,但不能直接处理实值数据。针对此问题,本文首先介绍了邻域和覆盖的概念,在此基础上构造了覆盖自约简和覆盖间约简(属性约简)算法;然后通过讨论邻域内各样本之间关系,提出了相斥元的定义,相斥元的存在可能导致决策正域计算错误,从而得到不符合数据表实际情况的属性依赖性,因此给出了分解相斥元的方法;最后在四个实值的基因表达数据库上进行了实验,结果表明该属性约简算法是有效的,并相对于现有其他算法具有较高的分类精度。

Numerical attribute reduction of mutex neighborhood covering rough set theory

Rough set theory is an effective method of knowledge reduction,but can not directly deal with numerical attributes.To address this problem,based on the concepts of neighborhood and cover,the algorithms of covering itself reduction and covering reduction are constructed.And then,by discussing the relationship among neighborhood samples,the mutex in neighborhood samples is defined.The unreasonable positive region caused by mutex,leads to attribute dependency that may not accord with what the real condition dataset reflects,thus the method of decompose mutex neighborhood is proposed.Finally,we report experimental results with four numerical gene expression datasets and compare the results with some other methods.The results prove that the proposed method is effective,and has higher tumor classification accuracy.