Two-stage Gene Selection for Support Vector Machine Classification of Microarray Data

摘要：

This paper proposes a new stable gene selection method for support vector machines (SVM) classification of microarray data,aiming to improve the classification accuracy.A two-stage algorithm is used to select genes,leading to the construction of a compact multivariate linear regression model,which contains only genes less than the number of experiments as well as a weight vector for each gene index.An SVM then learns the microarray data based on this linear regression model.The experimental results,from two well-known microarray data sets,show that SVMs with two-stage gene selection maintains a consistently high accuracy with a small number of genes.It is also shown that the proposed method outperforms the two other typical gene selection methods-Baseline Method and Significance Analysis of Microarrays in terms of accuracy.

关键词： Support Vector Machines Two-stage Linear Regression Gene Selection Baseline Method Significance Analysis of Microarrays.

作者: Xiao-Lei Xia Kang Li GeorgeW.Irwin

作者单位: School of Electronics,Electrical Engineering and Computer ScienceQueens University Belfast,Belfast School of Electronics,Electrical Engineering and Computer Science Queens University Belfast,Belfast

会议类型: 国际会议

会议名称: International Conference on Modelling,Identification and Control(模拟、鉴定、控制国际会议)

会议地点: 上海

会议语种:英文

在线出版日期: 2008-06-29（万方平台首次上网日期，不代表论文的发表时间）