A fast metric approach to feature subset selection

被引：0
|
作者：
Chan, TYT [1 ]
机构：
[1] Univ Aizu, Aizu Wakamatsu, Fukushima 9658580, Japan
来源：
24TH EUROMICRO CONFERENCE - PROCEEDING, VOLS 1 AND 2 | 1998年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A simple approach to feature subset selection is proposed. During the training stage, the method selects features that simultaneously minimize the within-class distance and maximize the between-class distance. Experiments performed on the Iris Plants Database and the Pima Indians Diabetes Database show that the approach is practical because it is fast and yet the correct classification rates are competitive.
引用
收藏
页码：733 / 736
页数：4
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