基于信息熵的核属性增量式高效更新算法  

Efficient Incremental Updating Algorithm for Core AttributeBased on Information Entropy

作　　者：钱文彬[1,2] 杨炳儒[1,2] 徐章艳[3] 张长胜[1,2] 

机构地区：[1]北京科技大学计算机与通信工程学院,北京100083 [2]北京科技大学材料领域知识工程北京市重点实验室,北京100083 [3]广西师范大学计算机科学与信息工程学院,桂林541004

出　　处：《模式识别与人工智能》2013年第1期42-49,共8页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60963008;60875029);国家973计划项目(No.2009CB522701);材料领域知识工程北京市重点实验室2012年度阶梯计划项目(No.Z121101002812005)资助

摘　　要：针对基于信息熵求核算法效率不理想的情况,给出信息观下的二进制差别矩阵定义,理论上证明基于信息熵的核属性与基于二进制差别矩阵的核属性等价;并将决策表划分为相容的对象集和不相容的对象集,缩小求核算法的搜索空间;然后针对动态的决策表,研究核属性的增量更新机制,由此构造一种基于信息熵的核属性增量式高效更新算法.实例分析与实验结果验证文中算法优于同类求解算法.Since the efficiency of the algorithms for core attribute based on information entropy is not well, the binary discernibility matrix from information view is defined. And it is proved theoretically that the core attribute based on the binary discernibility matrix is equivalent to that based on information entropy. The objects in the decision table are categorized into a consistent set and an inconsistent set, which effectively reduces the search space of algorithm for core attribute. Additionally, for dynamic decision table, the incremental updating mechanism for core attribute is discussed. Based on the mechanism, an efficient incremental updating algorithm for core attribute based on information entropy is proposed. The example analysis and experimental results show that the proposed algorithm outperforms other similar algorithms.

关 键 词：粗糙集 信息熵 核属性 增量更新 算法复杂度 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]