This thesis improves tumor samples classification of gene expression data in two aspects: classification algorithm and feature gene selection method.

针对基于基因表达数据的肿瘤样本分类，本文从分类算法和特征基因选取方法两个方面进行了改进。

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The problem of feature gene selection and tumor samples classification of microarray gene expression data is one of challenges of gene microarray technology.

基因表达数据的特征基因选取和肿瘤样本分类问题是基因微阵列技术的挑战性课题之一。

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This thesis improves classification using gene expression data method in two aspects: feature selection and SVMs classification algorithm.

针对基于基因表达数据的分类，本文从特征基因选择和支持向量机分类算法两个方面进行了改进。

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Now there are several feature selection methods applied to gene expression data, such as Sequential Forward Selection, GA, S2N and so on.

目前已有不少特征选取方法应用于基因表达数据，比如顺序前进法、遗传算法、信噪比指标等。

youdao

Now there are several feature selection methods applied to gene expression data, such as Sequential Forward Selection, GA, S2N and so on.

目前已有不少特征选取方法应用于基因表达数据，比如顺序前进法、遗传算法、信噪比指标等。

youdao