Optimality driven nearest centroid classification from genomic data.
Optimality driven nearest centroid classification from genomic data.
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DOI：
10.1371/journal.pone.0001002
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发表时间：
2007-10-03
期刊：
影响因子：
3.7
通讯作者：
Storey JD
中科院分区：
文献类型：
--
作者：
Dabney AR;Storey JD
最近的质心分类器已经成功地应用于高维应用，例如在基因组学中。在为高维数据构建分类器时，一个必要的步骤是特征选择。特征选择通常是通过单独计算每个特征的单变量分数来执行的，而不考虑特征子集作为一个整体的表现。我们介绍了一种新的高维最近质心分类器的特征选择方法，该方法基于理论上对给定数量的特征进行最优选择，我们在这里直接确定了这些特征。这使得我们可以开发一种新的贪婪算法来估计具有给定特征数量的最优最近中心分类器。此外，虽然质心通常由最大似然估计形成，但我们研究了高维收缩质心估计的适用性。我们将该方法应用于基于基因表达微阵列的临床分类，证明了该方法的性能优于现有的最近质心分类器。
Nearest-centroid classifiers have recently been successfully employed in high-dimensional applications, such as in genomics. A necessary step when building a classifier for high-dimensional data is feature selection. Feature selection is frequently carried out by computing univariate scores for each feature individually, without consideration for how a subset of features performs as a whole. We introduce a new feature selection approach for high-dimensional nearest centroid classifiers that instead is based on the theoretically optimal choice of a given number of features, which we determine directly here. This allows us to develop a new greedy algorithm to estimate this optimal nearest-centroid classifier with a given number of features. In addition, whereas the centroids are usually formed from maximum likelihood estimates, we investigate the applicability of high-dimensional shrinkage estimates of centroids. We apply the proposed method to clinical classification based on gene-expression microarrays, demonstrating that the proposed method can outperform existing nearest centroid classifiers.
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影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
5.8
作者：
Dabney, AR
通讯作者：
Dabney, AR
影响因子：
1.5
作者：
Bickel, PJ;Levina, E
通讯作者：
Levina, E
DOI：
10.1111/j.2044-8317.1982.tb00639.x
发表时间：
1982-01-01
影响因子：
2.6
作者：
MCKAY, RJ;CAMPBELL, NA
通讯作者：
CAMPBELL, NA
影响因子：
64.8
作者：
Alizadeh, AA;Eisen, MB;Staudt, LM
通讯作者：
Staudt, LM