可变直觉模糊多粒度粗糙集模型及其近似分布约简算法

为了在多粒度粗糙集模型中对目标概念达到更好的近似逼近效果，首先将直觉模糊粗糙集与多粒度粗糙集结合，提出直觉模糊多粒度粗糙集模型。由于该模型的目标近似存在过于宽松的缺陷，因此通过引入参数的方式对所提模型进行改进，提出一种可变直觉模糊多粒度粗糙集模型，并证明了该模型的有效性，同时基于该模型提出了相应的近似分布约简算法。在仿真实验结果中，所提出的下近似分布约简结果比已提出的模糊多粒度决策理论粗糙集约简和多粒度双量化决策理论粗糙集多了2~4个属性，所提出的上近似分布约简算法比这些算法少了1~5个属性，同时约简结果的近似精度拥有了更为合理且优越的表现。因此，理论和实验结果均验证了所提的可变直觉模糊多粒度粗糙集模型在近似逼近和数据降维方面均具有更高的优越性。

Variable intuitionistic fuzzy multi-granulation rough set model and its approximate distribution reduction algorithms

In order to obtain a better approximate approximation effect in multi-granulation rough set model for target conception, an intuitionistic fuzzy rough set and a multi-granulation rough set were combined together and a model of intuitionistic fuzzy multi-granulation rough set was proposed. Due to the loose defect of the target approximation of the model, a variable intuitionistic fuzzy multi-granulation rough set model was proposed by introducing parameters to improve the proposed model, and the validity of this model was proved. In addition, on the basis of this model, a corresponding approximate distribution reduction algorithm was also proposed. The simulation results show that, compared with the existing fuzzy multi-granulation decision-theoretic rough set and multi-granulation double-quantitative decision-theoretic rough set, the proposed lower approximation distribution reduction algorithm has 2 to 4 attributes more than that of them, and the proposed upper approximate distribution reduction algorithm has 1 to 5 attributes less than that of them; meanwhile, the approximation accuracy of reduction results is more reasonable and superior. Theoretical analysis and experimental results verify that the proposed variable intuitionistic fuzzy multi-granulation rough set model has higher superiority in terms of approximating approximation and reducing dimensions.