Entropy-based model-free feature screening for ultrahigh-dimensional multiclass classification

DOI10.1080/10485252.2016.1167206zbMath1349.62279OpenAlexW2333273157MaRDI QIDQ2832014

Lyu Ni, Fang Fang

Publication date: 4 November 2016

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485252.2016.1167206

zbMATH Keywords

entropyinformation gainsure screening propertymulticlass classificationfeature screening

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (13)

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Cites Work

• Model-Free Feature Screening for Ultrahigh-Dimensional Data
• Sure independence screening in generalized linear models with NP-dimensionality
• Robust rank correlation based screening
• High-dimensional classification using features annealed independence rules
• Class prediction by nearest shrunken centroids, with applications to DNA microarrays.
• Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
• The fused Kolmogorov filter: a nonparametric model-free screening method
• Forward Regression for Ultra-High Dimensional Variable Screening
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
• Feature Screening via Distance Correlation Learning
• Feature Selection for Varying Coefficient Models With Ultrahigh-Dimensional Covariates
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models
• Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis
• The Kolmogorov filter for variable screening in high-dimensional binary classification

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