基于粗集边界域的快速约简算法

摘要

属性约简是粗糙集研究的核心内容之一.已有的大多数属性约简算法都是采用基于正域的贪心算法求决策表的代数约简.事实上,对于不一致决策表,代数约简改变了决策类族原有的Pawlak拓扑结构,造成决策类的不确定性扩大.为此,提出了一种新的基于粗集边界域的约简模型,它能够保持决策类族原有的Pawlak拓扑结构.依据新模型,提出了一种高效率的基于粗集边界域的属性约简算法.理论分析和实验表明,所提算法是有效可行的.%Attribute reduction is one of the core research content of Rough set. Most of the existing greedy reduction algorithm is based on positive region to find out an algebraic reduct. In fact, for an inconsistency decision table,algebra reduct changes the original Pawlak topology and expands the uncertainty degree of decision table. Therefore, in this paper, a novel reduction modal based on rough boundary region was introduced, which can keep the original Pawlak topology. Based on this model,an efficient algorithm for attribute reduction based on rough boundary region was proposed. Theoretical analysis and experimental results show that the algorithm of this paper is effective and feasible.