癌症基因表达数据的集成分类器设计与分析

摘要

Gene expression levels are important for disease, such as, Cancer diagnosis. This paper proposed a SVM-based ensemble classiifer to classify the control and cancer groups based on gene expression levels from microarray data. A combinational Recursive Feature Elimination in conjunction with the Adaboost algorithm was developed to select significant features and design the proper classiifer. The method is applied to microarray data of cancer patients, and the results show improvements on the success rate.%基因表达水平对癌症诊断起到重要的作用。文章提出了一种基于SVM（Support Vector Machine）的集成分类算法，从基因表达水平的微阵列数据中对癌症和正常群体进行分类。文章提出了一种结合Adaboost算法和递归特征消除（Recursive Feature Elimination，RFE）算法，选取最显著的特征并设计与之适合的分类器。该方法已应用于癌症病人的基因表达微阵列数据的分类中，其分类结果在成功率方面有极大的提升。