Statistical inference for $$L^2$$ L 2 -distances to uniformity

Baringhaus, Ludwig; Gaigall, Daniel; Thiele, Jan Philipp

doi:10.1007/s00180-018-0820-0

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Mai and Zou [10] used a Kolmogorov filter (KF) method, which is also applied for the ultrahigh-dimensional binary classification problem with a dependent variable in Lai et al [11]. Roy et al [12] proposed a model-free feature screening method based on energy distances (see [13,14]). Note that all the papers cited in this paragraph deal with binary classification only.…”

Section: Introductionmentioning

confidence: 99%

Feature space reduction method for ultrahigh-dimensional, multiclass data: random forest-based multiround screening (RFMS)

Hanczár,

Stippinger,

Hanák

et al. 2023

Mach. Learn.: Sci. Technol.

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In recent years, several screening methods have been published for ultrahigh-dimensional data that contain hundreds of thousands of features, many of which are irrelevant or redundant. However, most of these methods cannot handle data with thousands of classes. Prediction models built to authenticate users based on multichannel biometric data result in this type of problem. In this study, we present a novel method known as random forest-based multiround screening (RFMS) that can be effectively applied under such circumstances. The proposed algorithm divides the feature space into small subsets and executes a series of partial model builds. These partial models are used to implement tournament-based sorting and the selection of features based on their importance. This algorithm successfully filters irrelevant features and also discovers binary and higher-order feature interactions. To benchmark RFMS, a synthetic biometric feature space generator known as BiometricBlender is employed. Based on the results, the RFMS is on par with industry-standard feature screening methods, while simultaneously possessing many advantages over them.

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