A hybrid GA/SVM approach for gene selection and classification of microarray data

被引：0

作者：

Huerta, Edmundo Bonilla ^{[1
]}

Duval, Beatrice ^{[1
]}

Hao, Jin-Kao ^{[1
]}

机构：

[1] Univ Angers, LERIA, F-49045 Angers, France

来源：

APPLICATIONS OF EVOLUTIONARY COMPUTING, PROCEEDINGS | 2006年 / 3907卷

关键词：

Genetic Algorithms; fuzzy logic; Support Vector Machines; feature selection; classification; microarray data;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We propose a Genetic Algorithm (CA) approach combined with Support Vector Machines (SVM) for the classification of high dimensional Microarray data. This approach is associated to a fuzzy logic based pre-filtering technique. The CA is used to evolve gene subsets whose fitness is evaluated by a SVM classifier. Using archive records of "good" gene subsets, a frequency based technique is introduced to identify the most informative genes. Our approach is assessed on two well-known cancer datasets and shows competitive results with six existing methods.

引用

页码：34 / 44

页数：11

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