最小相关性最大依赖度属性约简

Attribute Reduction with Principle of Minimum Correlation and Maximum Dependency

在经典粗糙集中,基于重要度的决策表属性约简算法只考虑了决策属性与条件属性之间的依赖度,没有考虑约简中条件属性之间的相关性,由此求出的约简中可能依然包含冗余属性。针对这一问题,提出了一种改进算法,它利用最小相关性和最大依赖度准则求决策表属性约简。与基于重要度的决策表属性约简算法相比,本算法求出的约简包含的属性个数少、冗余小。实验结果显示,本算法优于基于重要度的决策表属性约简算法。

In the classical rough set,the reduction algorithm based on significance for decision table only considers the dependency of decision attribute and condition attribute,and does not consider the correlation between the condition attributes in reduct.The reduct calculated with this kind of algorithm may include redundant attributes.In order to deal with this problem,an improved algorithm was proposed in this paper,which calculates the reduct with the principle of minimum correlation and maximum dependency.Compared with the reduction algorithm based on significance for decision table,less attributes are remained in the reducts calculated with the proposed algorithm,and the redundancy of the reduct is smaller.The experimental results show that the proposed algorithm outperforms the reduction algorithm based on significance for decision table.