摘要
决策粗糙集基于严格的不可分辨等价关系,只能适用于离散型数据,文中研究了一种新的模糊决策粗糙集模型及相应的属性约简算法.该模型将不可分辨等价关系放松为高斯核模糊T-等价关系,从模糊隶属度角度定义了条件概率,能够直接对数值型数据进行属性约简.利用UCI标准数据集,将该模型与Pawlak经典粗糙集、决策粗糙集在属性约简能力上进行比较,仿真实验结果表明,该模型具有较好的性能.
The DTRS is based on strict indiscernibility relation,therefore,it can only be applied to discretized data.In consequence,a fuzzy decision-theoretic rough set（FDTRS） model and a forward greedy attribute reduction algorithm were proposed based on the FDTRS model.The FDTRS model generalizes the indiscernibility relation to fuzzy T-equivalence relations based on Gaussian kernel and defines the conditional probability from the perspective of degree of fuzzy membership.The FDTRS can deal with numerical data directly.Four UCI data sets were used to compare the performance of the FDTRS with Pawlak rough set and decision-theoretic rough set on attribute reduction.Experimental results help quantify the performance of the FDTRS.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2013年第7期1032-1035,1042,共5页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金资助项目(70971062)
东南大学复杂工程系统测量与控制教育部重点实验室开放课题基金项目(2010A004)
关键词
模糊决策粗糙集
条件概率
数值型属性
属性约简
fuzzy decision-theoretic rough set
conditional probability
numerical attribute
attribute reduction
