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Feature selection in omics prediction problems using cat scores and false nondiscovery rate control. (English) Zbl 1189.62102

Summary: We revisit the problem of feature selection in linear discriminant analysis (LDA), that is, when features are correlated. First, we introduce a pooled centroids formulation of the multiclass LDA predictor function, in which the relative weights of Mahalanobis-transformed predictors are given by correlation-adjusted \(t\)-scores (cat scores). Second, for feature selection we propose thresholding cat scores by controlling false nondiscovery rates (FNDR). Third, training of the classifier is based on James-Stein shrinkage estimates of correlations and variances, where regularization parameters are chosen analytically without resampling. Overall, this results in an effective and computationally inexpensive framework for high-dimensional prediction with natural feature selection. The proposed shrinkage discriminant procedures are implemented in the R package “sda” available from the R repository CRAN.

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)
62H20 Measures of association (correlation, canonical correlation, etc.)
65C60 Computational problems in statistics (MSC2010)

Software:

CMA; R; rda; scout; fdrtool; sda; corpor
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References:

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