关键词: 基因表达谱, 多分类问题, 基因选择, 支持向量机

Abstract: Support Vector Machine—Recursive Feature Elimination（SVM-RFE） is one of state-of-the-art method for gene selection.SVM-RFE was originally designed to solve binary feature selection problems and has been extended to solve multiclass problems in several recent studies.This paper illustrates the limitations of the present multi-class gene selection methods from the perspective of Pareto Optimum，describes a new procedure for selecting significant genes for each class，and proposes a new implementation for SVM-RFE. Experiments on 8 cancer and tumor gene expression dataset demonstrate its superiority over two other RFE methods.By considering each class during the gene selection stages，the new method can identify genes leading to more accurate classification.

Key words: gene expression data, multi-classification, gene selection, support vector machine