一种变相似度的模糊粗糙集属性约简  

Attribute Reduction of Fuzzy Rough Sets Based on Variable Similar Degree

作　　者：张慧哲[1] 王坚[1] 梅宏标[1] 

机构地区：[1]同济大学CIMS研究中心,上海201804

出　　处：《模式识别与人工智能》2009年第3期393-399,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家科技支撑计划资助项目(No.2006BAF01A46)

摘　　要：将模糊粗糙集作为经典粗糙集的一种模糊推广,把模糊集合相似度引入模糊粗糙集模型中,提出一种基于变相似度的模糊粗糙集模型.通过定义模糊相似矩阵和不一致程度矩阵,给出知识约简的相关概念,即属性约简、核以及属性约简的算法,并证明模糊最小约简和核之间的关系.通过实例证实,对相似精度进行调节,在获得多个层次上的属性归约集的同时保证分类准确性,增加对信息的灵活掌握.最后对本文算法和紧计算域的模糊粗糙集算法进行对比测试,结果验证采用本文算法的约简结果具有较高的分类准确率,为解决粗糙集中连续属性的属性约简问题提供一个有效的方法.A fuzzy rough model based on variable similar degree is presented. It introduces the fuzzy similar degree and extends classical Pawlak＇s model. Firstly, the definitions of fuzzy similarity matrix and fuzzy difference degree matrix are given. Then, in the light of these definitions, the concepts of knowledge reduction are provided, such as attribute reduction, core, and algorithm of attribute reduction. Moreover, the relation between minimum reduction and core is proved. The proposed method obtains attribute reduction sets at different levels. Meanwhile, it keeps the classification accuracy with better flexibility by adjusting the similar precision. Compared to compact computational domain of fuzzy rough set, the proposed method has better classification accuracy and it provides a new idea for continued attribute data reduction.

关 键 词：模糊粗糙集 属性约简 核 模糊相似度 模糊相似关系 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]