基于广义变精度粗糙模糊集模型的知识发现

Knowledge Discovery Approach Based on Generalized Variable Precision Rough Fuzzy Set Model

介绍了广义Ziarko’s变精度粗糙集模型和广义粗糙模糊集模型,找出了它们的不足。基于支集相对错误分类率及误差参数β(0≤β<0.5),提出了广义变精度粗糙模糊集模型,讨论了模型中β上、下近似算子的性质;分析了该模型与广义Pawlak’s粗糙集模型、广义Ziarko’s变精度粗糙集模型和广义粗糙模糊集模型的关系;最后给出了该模型中近似约简的定义和方法,并通过实例分析说明了约简算法的有效性。

Generalized Ziarko＇ s variable precision rough set model and generalized rough fuzzy set model were introduced and the drawbacks of them were found. Based on support relative error ratio and error parameter β（0≤β〈0. 5）, the generalized variable precision rough fuzzy set model was proposed. The basic properties of approximation operators were investigated. The relation of between this model and generalized Pawlak＇s rough set model, generalized Ziarko＇ s variable precision rough set model and generalized rough fuzzy set model was analysed in detail. Finally, the definitions and the approaches of approximation reduction were discussed, an example was given to illustrate the validity of the presented algorithms.