关键词: 基因表达谱, 肿瘤分类, 基因选择, 支持向量机, 等价类

Abstract: The key of distinguishing between normal and tumor samples effectively for tumor gene expression data is to find out the fewest genes which can predict the classes, then use a good performance classifier to classify. Faced with the problem, it uses the Revised Feature Score Criterion（RFSC） to remove the genes irrelevant to the classification task. It improves the pair-wise redundancy method, proposes strong correlative tree to filter the redundant gene. It improves the Rough Support Vector Machine（RSVM） and proposes the Approximate Equivalence Rough Support Vector Machine（AE-RSVM）, and then validates classification for data sets. Using the tumor data set to test, the experimental results show the feasibility and effectiveness of the method proposed in this paper．

Key words: gene expression profile, tumor classification, gene selection, support vector machine, equivalence class