基于邻域近似条件熵的启发式属性约简  

Heuristic attribute reduction based on neighborhood approximate conditional entropy

作　　者：张宁 范年柏 Zhang Ning;Fan Nianbai(College of Computer Science&Electronic Engineering,Hunan University,Changsha 410082,China)

机构地区：[1]湖南大学信息科学与工程学院,长沙410082

出　　处：《计算机应用研究》2018年第5期1395-1398,共4页Application Research of Computers

基　　金：湖南省科技计划应用基础研究重点项目(2016JC2014)

摘　　要：目前粗糙集的研究局限于有限集,且现有的邻域粗糙集属性约简算法中属性重要性度量方式单一。针对邻域粗糙集存在的问题,提出了基于无限集的邻域近似条件熵模型。该模型以邻域近似条件熵下的属性重要度为启发条件,构造了一种基于邻域近似条件熵的前向贪心搜索属性约简算法。利用熵的单调性,证明了算法的正确性,并分析了算法的时间复杂度。通过实例分析和多个UCI数据集上的实验表明,所提出的算法是可行的,能有效减少属性数量,与现有的算法相比,不仅能够获得较小的属性约简结果,而且具有较好的分类性能。The research of the rough set has been limited to the finite set so far,and the attribute significance measurement is single in attribute reduction algorithm based on the neighborhood rough set model.In order to solve the problem in neighborhood rough set,this paper proposed the neighborhood approximate conditional entropy model based on infinite set.Furthermore,it constructed a forward greedy attribute reduction algorithm which used the neighborhood approximate conditional entropy as heuristic condition in this model.The monotonicity of the entropy proved the correctness of the algorithm,and the time complexity was analyzed.The instance analysis and the experimental results of UCI data sets show that the proposed attribute reduction algorithm is feasible and it can reduce the number of attributes effectively.It can get smaller attribute reduction results,and better classification power.

关 键 词：邻域粗糙集 条件熵 属性约简 属性重要性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]