基于邻域熵的决策表约简  

Decision Table Reduction Based on Neighborhood Entropy

作　　者：陈玉明[1] 吴克寿[1] 唐朝辉[1] 

机构地区：[1]厦门理工学院计算机科学与技术系,福建厦门361024

出　　处：《闽南师范大学学报（自然科学版）》2014年第2期42-47,共6页Journal of Minnan Normal University：Natural Science

基　　金：国家自然科学青年基金项目(61103246)

摘　　要：针对传统粗糙集理论难以处理数值型数据的特点,提出基于邻域熵的决策表特征约简方法.该方法通过引入邻域关系进行信息粒化,定义邻域熵概念,用来度量数值型数据的不确定性,证明邻域熵的单调性原理,提出基于邻域熵与分类精度加权的特征重要度概念,基于邻域熵单调性原理设计了两种启发式特征约简算法.理论分析与实例表明该方法是有效可行的.In view of the fact that the classical rough set theory was difficult to deal with the real data, a feature reduction method was proposed based on neighborhood entropy in the decision table. By the definitions of neighborhood relation, each object in the universe was assigned with a neighborhood subset, called neighborhood granule. The concept of neighborhood entropy was defined to measure uncertainty of real data. The monotonicity of neighborhood entropy was proved. Furthermore, the combination of neighborhood entropy and classification accuracy was used to evaluate the significance of attributes and two heuristic feature reduction algorithms were constructed. Theoretical analysis and an example show that the reduction method is efficient and feasible.

关 键 词：粗糙集 邻域熵 约简 决策表 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]