广义粗糙集理论及实值属性约简

Generalization rough set theory and real-valued attributes reduction

针对经典粗糙集理论仅能处理离散化数据的局限性,提出属性和属性子集的广义重要度的概念以及空间中的广义近邻关系,并提出了广义近邻关系下的广义粗糙集扩展模型。广义粗糙集理论利用广义近邻关系在全局中划分相容模块,构成集合的下、上近似集,避免了经典粗糙集理论必须量化数据的麻烦。另外,提出了广义粗糙集的实值属性约简的一种贪心算法,并分析了约简属性集合的质量。最后通过实例验证了所提方法的正确性和有效性。

Considering that the classical rough sdt theory can only process the discrete data, the degree of general importance of an attribute and attribute subsets was presented. And then a generalization rough set theory was proposed based on the general near neighborhood relation. The theory partitioned the universe into the tolerant modules and formed lower approximation and upper approximation of the set under general near neighborhood relationship which avoided the discretization in Pawlak＇s rough set Furthermore, the definition of attribute reduction in generalization rough set and its greedy algorithm were proposed. Finally, results of some examples show the correctness and validity of this method.