基于RSKNN分类改进算法  

Improved RSKNN Algorithm for Classification

作　　者：兰天[1,2] 郭躬德[1,2] 

机构地区：[1]福建师范大学数学与计算机科学学院,福州350007 [2]福建师范大学网络安全与密码技术福建省重点实验室,福州350007

出　　处：《计算机系统应用》2013年第12期85-92,共8页Computer Systems & Applications

基　　金：国家自然科学基金(61070062;61175123);福建高校产学合作科技重大项目(2010H6007)

摘　　要：RSKNN算法是K近邻算法的一种改进算法,该算法基于变精度粗糙集理论,能在保证一定分类精度的前提下,有效地降低分类样本的计算量,并且提高计算效率和分类精度.由于RSKNN算法对属性的依赖度较高,在分类时容易受到伪近邻的影响,导致RSKNN算法的分类精度受到一定程度的影响.针对存在问题,本文提出一种新颖的基于RSKNN算法的改进算法SMwRSKNN,该算法在RSKNN算法的基础上引入类别子空间的思想,以降低冗余属性和伪近邻对分类的影响.在UCI公共数据集上的实验结果表明,SMwRSKNN算法比RSKNN算法具有更高的分类精度.RSKNN is an improved algorithm of KNN with better classification performance. The RSKNN algorithm is based on the theory of the variable precision rough set. The algorithm guarantees under the premise of a certain classification accuracy, effectively reduces the computation burden of the classified samples, and improves the computation efficiency and precision of classification. But the degree of dependence on attributes is very high, which can make RSKNN algorithm affected by a certain degree of precision in classification. So the use of the class subspace classification method into RSKNN algorithm can improve the classification accuracy of RSKNN. The experimental results carded out on some UCI public datasets verify the effectiveness of the proposed algorithm.

关 键 词：变精度粗糙集 类别上 下近似 互K近邻 类别子空间 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]