High-dimensional bolstered error estimation.
High-dimensional bolstered error estimation.
复制标题
高维支持误差估计。
DOI：
10.1093/bioinformatics/btr518
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Dougherty,EdwardR
中科院分区：
文献类型：
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作者：
Sima,Chao;Braga-Neto,UlissesM;Dougherty,EdwardR
动机：在小样本环境中，支持误差估计已被证明比交叉验证更好，并且在各种标准方面与自助法具有竞争力。支持性能的关键问题是支持内核的方差设置。因此，该方差是根据数据以非参数方式确定的。虽然基于此方差设置的支撑对于小特征集效果良好，但对于高维feature spaces.Results，结果可能会恶化：本文根据分类规则，样本大小，模型和特征空间，原始数量和特征选择后剩余的数量计算最佳核方差。一个关键点是最优方差相对于模型是稳健的。这使我们能够开发一种方法来选择一个合适的方差，用于实际应用中，其中模型是未知的，但确定最佳内核的其他因素是已知的。可用性：Companion website at http://compbio.tgen.org/paper_supp/high_dim_bolsteringContact:edward@mail.ece.tamu.edu
Motivation:In small-sample settings, bolstered error estimation has been shown to perform better than cross-validation and competitively with bootstrap with regard to various criteria. The key issue for bolstering performance is the variance setting for the bolstering kernel. Heretofore, this variance has been determined in a non-parametric manner from the data. Although bolstering based on this variance setting works well for small feature sets, results can deteriorate for high-dimensional feature spaces.Results:This article computes an optimal kernel variance depending on the classification rule, sample size, model and feature space, both the original number and the number remaining after feature selection. A key point is that the optimal variance is robust relative to the model. This allows us to develop a method for selecting a suitable variance to use in real-world applications where the model is not known, but the other factors in determining the optimal kernel are known.Availability:Companion website at http://compbio.tgen.org/paper_supp/high_dim_bolsteringContact:edward@mail.ece.tamu.edu