变精度粗糙集的区域属性约简及其结构启发算法

采用二分类情形与三支决策区域，研究变精度粗糙集（VPRS）两类属性约简及其结构启发算法。首先，依托三支决策区域构建分类区域，提出分类区域保持（CRP）约简与决策区域保持（DRP）约简，得到对定性属性约简的量化扩张性，设计基于核的结构启发算法；然后，研究两类区域约简的强弱关系，设计由强至弱的结构启发算法，得到二支决策拓展为三支决策的约简改进；最后，利用数据表实例与UCI数据集验证区域约简及其启发算法的有效性。

Regional attribute reduction and their structural heuristic algorithms for variable precision rough sets

According to the two-category case and three-way decision regions, two types of attribute reductions for Variable Precision Rough Sets (VPRS) and their structural heuristic algorithms were studied. First of all, classification-regions were constructed by three-way decision regions, and Classification-Region Preservation (CRP) reduction and Decision-Region Preservation (DRP) reduction were proposed, quantitative expansion of the qualitative attribute reduction was obtained, and the structural heuristic algorithms based on cores were designed. Furthermore, the strong-weak relationships between the two kinds of regional reductions were studied, and structural heuristic algorithms from strong to weak were designed to achieve improvement from the two-way to three-way decisions. Finally, the validity of the relevant reductions and algorithms were verified by the data table and UCI data set.