邻域双量化粗糙模糊集近似属性约简

Approximate Attribute Reduction for Neighborhood-based Double-quantitative Rough Fuzzy Set

本文以邻域双量化粗糙模糊集为研究对象,探究其近似属性约简的数学表示,提出一种复杂度更低的新型遍历式属性约简算法。在数值实验中提出相对重构正确率的概念,并且将双量化模型与单量化模型的约简结果进行对比分析。

In this paper,we take neighborhood-based double-quantitative rough fuzzy set as target and study the mathematical representation of it.Furthermore,we propose a novel ergodic attribute reduction algorithm with lower complexity.In the numerical experiment,we present the concept of relative reconstruction accuracy and then compare the reduction results of double-quantitative models and single-quantitative models.