Feature selection based on geometric distance for high-dimensional data

被引：3

作者：

Lee, J. -H. ^{[1
]}

Oh, S. -Y. ^{[1
]}

机构：

[1] Pohang Univ Sci & Technol, Dept Elect Engn, Pohang, South Korea

来源：

ELECTRONICS LETTERS | 2016年 / 52卷 / 06期

关键词：

feature selection; set theory; statistical analysis; geometric distance; high-dimensional data; feature selection method; feature subset evaluation; feature evaluation process; feature selection process; geometrical analysis; statistical dependency concepts; information dependency concepts;

D O I：

10.1049/el.2015.4172

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A novel feature selection method based on geometric distance is proposed. It utilises both the average distance between classes along with the evenness of these distances to evaluate feature subsets. The feature evaluation and selection process used therein is very easy to understand, because it lends itself to a simple geometrical analysis. Moreover, because the method does not calculate the relevance or redundancy between features, it is faster than other filter methods that use information or statistical dependency concepts. The experiments demonstrate its markedly better classification performance as well as fast computation compared with existing methods.

引用

页码：473 / 474

页数：2