Small sample issues for microarray-based classification.
Small sample issues for microarray-based classification.
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DOI：
10.1002/cfg.62
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发表时间：
2001
影响因子：
--
通讯作者：
Dougherty, E R
中科院分区：
文献类型：
--
作者：
Dougherty, E R
为了研究正常和病变组织之间的分子生物学差异，需要使用基于微阵列的基因表达值对疾病和疾病阶段进行分类。由于这些研究中通常使用的微阵列数量有限，在基于微阵列数据的分类器的设计、性能和分析方面出现了严重的问题。本文综述了小样本分类面临的一些基本问题：分类规则、约束分类器、误差估计和特征选择。讨论了基于样本数据的无约束和约束分类器设计，以及基于样本数据设计的约束优化和缺乏最优性对分类器误差的贡献。解决了在小样本情况下估计分类器误差的困难，特别是从训练数据估计误差。解释了小样本对包含多个变量作为分类器特征的能力的影响。
In order to study the molecular biological differences between normal and diseased tissues, it is desirable to perform classification among diseases and stages of disease using microarray-based gene-expression values. Owing to the limited number of microarrays typically used in these studies, serious issues arise with respect to the design, performance and analysis of classifiers based on microarray data. This paper reviews some fundamental issues facing small-sample classification: classification rules, constrained classifiers, error estimation and feature selection. It discusses both unconstrained and constrained classifier design from sample data, and the contributions to classifier error from constrained optimization and lack of optimality owing to design from sample data. The difficulty with estimating classifier error when confined to small samples is addressed, particularly estimating the error from training data. The impact of small samples on the ability to include more than a few variables as classifier features is explained.