High dimensional classifiers in the imbalanced case

DOI10.1016/J.CSDA.2015.12.009zbMath1468.62021OpenAlexW2220926743MaRDI QIDQ1659246

Jens Ledet Jensen, Britta Anker Bak

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2015.12.009

zbMATH Keywords

classificationimbalancehigh dimension

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (3)

Ensemble decision forest of RBF networks via hybrid feature clustering approach for high-dimensional data classification ⋮ Difficulty factors and preprocessing in imbalanced data sets: an experimental study on artificial data ⋮ New hard-thresholding rules based on data splitting in high-dimensional imbalanced classification

Cites Work

• Unnamed Item
• High-dimensional classification using features annealed independence rules
• Gradient boosting for high-dimensional prediction of rare events
• Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
• A Direct Estimation Approach to Sparse Linear Discriminant Analysis
• Feature selection by higher criticism thresholding achieves the optimal phase diagram
• Classification Error of the Thresholded Independence Rule
• A Road to Classification in High Dimensional Space: The Regularized Optimal Affine Discriminant

This page was built for publication: High dimensional classifiers in the imbalanced case