摘要
不确定性是粗糙集理论研究中的热点问题之一,而知识粒度是度量知识系统不确定性的一种重要方法.文中从矩阵的视角探讨知识粒度、粗糙度和属性重要度等概念的计算方法并分析知识粒度矩阵算式的内在含义,揭示出知识粒度与等价关系矩阵之间的关系.在提出知识粒层次结构的基础上进一步探讨了属性增删时知识粒度的变化规律.最后结合属性增删时不可分辨关系矩阵的更新将属性重要度的矩阵计算方法应用于求属性集的核集和最小约简中,算例表明属性重要度的矩阵计算方法在属性约简中的有效性.
The uncertainty is one of the hot issues in rough set theory. Knowledge granulation is a main approach to measure the uncertainty of knowledge systems. From the viewpoint of matrix, an approach for calculation of knowledge granulation, discernibility degree and attribute importance is studied, and the inherent meaning of matrix expression for the knowledge granulation is analyzed. Furthermore, the relations between the knowledge granulation and the equivalent relation matrix are revealed. Moreover, the hierarchical structure of knowledge granulation is proposed to discuss the variation of knowledge granulation under the addition or removal of a single attribute. Finally, combined with the update of the equivalent relation matrix under addition or removal of a signal attribute, the matrix-based computing method for attribute importance is applied in calculating the core set and the minimum attribute reduction. The numerical examples demonstrate the effectiveness of the proposed method on attribute reduction.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2013年第5期447-453,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61175047
61100117)
江西省自然科学基金项目(No.2011ZBAB201005)
江西省教育厅科学技术研究项目(No.GJJ13763)资助
关键词
知识粒度
属性重要度
约简
矩阵
Knowledge Granulation, Attribute Importance, Reduction, Matrix
