A GA-based optimal gene subset selection method

被引：1
|
作者：
Ding, SC [1 ]
Liu, J [1 ]
Yang, Q [1 ]
机构：
[1] Wuhan Univ, Comp Sch, Wuhan 430079, Hubei, Peoples R China
来源：
PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2004年
关键词：
optimal gene subset; genetic algorithms; fitness function; microarray;
D O I：
10.1109/ICMLC.2004.1378505
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In tissue diagnosis with microarray data, selecting the optimal gene subset is very essential for various merits. Different from existing methods, we propose a novel optimal gene subset selection method based on genetic algorithms (GAs). Special fitness function is applied in this scheme. This GA-based method automatically determines the size of a predictive gene group, as well as the members in such a group. The evaluation experiments are applied to two popular data sets. The results and some discussions are presented too.
引用
收藏
页码：2784 / 2789
页数：6
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