Support vector machine and optimal parameter selection for high-dimensional imbalanced data

DOI10.1080/03610918.2020.1813300OpenAlexW3084383058MaRDI QIDQ5055167

Yugo Nakayama

Publication date: 13 December 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2020.1813300

zbMATH Keywords

classificationsupport vector machineHDLSSimbalanced data

Mathematics Subject Classification ID

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

Cites Work

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• Distance-Weighted Discrimination
• A distance-based, misclassification rate adjusted classifier for multiclass, high-dimensional data
• Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings
• Geometric insights into support vector machine behavior using the KKT conditions
• Support vector machine and its bias correction in high-dimension, low-sample-size settings
• Scale adjustments for classifiers in high-dimensional, low sample size settings
• A survey of high dimension low sample size asymptotics
• Weighted Distance Weighted Discrimination and Its Asymptotic Properties
• Geometric Representation of High Dimension, Low Sample Size Data

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