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.