一种基于相容粗糙集的特征选择算法研究

A Feature Selection Based on Tolerance Rough Set Theory

粗糙集是一个去掉冗余特征的有效工具.由于大量真实数据的连续性,为了避免运用粗糙集方法所必须的离散化过程带来的信息丢失,将相容粗糙集应用于特征选择,并对粗糙集的边界域进行研究,提出了一种基于相容粗糙集的特征选择算法.在标准数据集上进行实验,结果表明本文提出的相容粗糙集特征选择方法是有效的.

Rough set theory is an efficient mathematical tool for further reducing redundancy. The main limitation of traditional rough set theory is the lack of effective methods for dealing with real-valued data. However, most of data sets are always continuous. This has been addressed by employing discretization methods, which may restdt in information loss. This paper investigates one approach combining the information contained within the boundary region with features selection based on tolerance rough set theory. Compared with gene selection algorithm based on rough set theory, the proposed method is more effective.