Incorporating gene similarity into support vector machine for microarray classification and gene selection

摘要：

In this paper, we propose a novel method based on support vector machine (SVM) for microarray classification and gene (feature) selection. The proposed method, called similaritybased SVM (SSVM), incorporates the prior knowledge of gene similarity into the standard SVM by combining the standard l2 norm and the similarity penalty of all the genes. The preliminary experiments show that our method performs better than the standard SVM, l2 l0 SVM and SVMRFE, especially when the features are highly similar.

关键词： Support vector machine microarray data gene selection

作者: Jun-Yan Tan Zhi-Xia Yang

作者单位: College of Science, China Agricultural University, Beijing 100083 College of Mathematics and System Science, Xinjiang University, Urumuchi 830046 Academy of Mathemati

会议类型: 国际会议

会议名称: The Second International Symposium(OSB08)(第二届国际优化及系统生物学学术会议)

会议地点: 云南丽江

会议语种:英文

页码: 350-357

在线出版日期: 2008-10-31（万方平台首次上网日期，不代表论文的发表时间）