采用非线性核支持向量机并基于基因表达数据的基因选择和分类

在基于微阵列的癌症分类中,由于变量(基因表达)较多,而实验条件较少,因此特征选择和分类方法非常重要。对于疾病诊断,分类器的性能直接影响到最终结果的准确性。本文提出一种新的基因选择和分类方法,这种方法使用基于递归特征排除(RFE)的非线性核支持向量机(SVM)。实验表明本文方法比其它线性分类方法具有更好的整体表现,如线性核支持向量机和Fisher线性判别分析方法;同样本文方法也比一些非线性分类方法更好,如采用非线性核的最小二乘支持向量机(LS-SVM)。实验除了使用测试集,还使用留一校验算法(leave-one-out)用于测试分类器的泛化性能。实验采用可通过互联网获得的AML/ALL数据集和遗传性乳腺癌数据集。

Gene Selection and Classification Using Non-linear Kernel Support Vector Machines Based on Gene Expression Data

In microarray-based cancer classification, feature selection and classification method is an important issue owing to large number of variables (gene expressions) and small number of experimental conditions. For disease diagnosing, classifiers' performance has direct impact on final results. In this paper, a new method of gene selection and classification by using non-linear kernel support vector machine(SVM) based on recursive performance elimination(RFE) is proposed. It is demonstrated experimentally that our method has better comprehensive performance than other linear classification methods, such as linear kernel support vector machine and fisher linear discriminant analysis (FLDA), also better than some non-linear classification methods, such as least square support vector machine(LS-SVM) using non-linear kernel. In the experiments, besides test set, leave-one-out algorithm is also used to test the classifiers' generalization performance. AML/ALL dataset and hereditary breast cancer dataset are used, which are available on internet.