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Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification

Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification

We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This …