摘要
传统的单核粒化粗糙集模型没有考虑不同粒化关系的相互影响。为解决这一问题,提出了基于多核粒化的模糊粗糙计算模型。以一族核关系构成的多粒度空间为研究对象,将乐观和悲观粗糙集模型拓展到多核空间,定义了基于S-T算子的多核下、上近似算子。给出了基于多核粒化粗糙逼近的属性选择算法。实验结果验证了不同核粒化关系之间"求同存异"和"求同排异"的相互作用。
The classical single kernelized rough set model ignores the interaction between different kernelized relations. In order to solve this problem, this paper is devoted to the construction of the fuzzy rough set model based on multi-kernelized granulation. In this study, the optimistic and pessimistic rough set model, which is derived from a family of the kernelized relations, is deeply explored to multi-kernelized granulation space by defining theS-T multi-kernelized lower and upper approximation operators. Finally, we apply these measures to evaluate and select features of classification problems. The experimental results verify the interaction in different granulating relations.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2014年第5期717-723,共7页
Journal of University of Electronic Science and Technology of China
基金
国家863项目(2008AA04A107)
国家科技支撑计划基金(2009BAH46B0302)
关键词
近似算子
属性选择
模糊粗糙集
多核粒化
approximation operator
feature selection
fuzzy rough set
multi-kernelized granulation
