• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2005, Vol. 27 ›› Issue (11): 57-58.

• 论文 • 上一篇    下一篇

一种基于粗糙集的混合特征选择算法

彭佳红   

  • 出版日期:2005-11-01 发布日期:2010-06-24

  • Online:2005-11-01 Published:2010-06-24

摘要:

本文在基于粗糙集理论的最小差异表MDL上,使用增量方式构造了与MDL相类似的简单差异矩阵SDM,以SDM近似约简集为起点对属性子集空间进行前向搜索,提出了一种基于粗 糙集的混合特征选择算法。该算法大大提高了特征选择的效率和准确性,适用于数据挖掘的预处理过程。

关键词: 特征选择 数据挖掘 KDD 粗糙集

Abstract:

In this paper, based on the Minimum Different List (MDL) of the rough set theory, we build a Simple Different Matrix (SMD) which is similar to MDLusing the incremental representation. From the approximate reduction set of SDM, we implement the forward selection of the attribute subspace. Finally,a hybrid feature selection algorithm based on the rough set theory has been proposed, and the result shows that the efficiency and accuracy of feature selection have been improved greatly by this approach. What is more, the algorithm does well in the preprocessing of data mining.

Key words: (feature selection, data mining, KDD, rough set)