LoRAS: an oversampling approach for imbalanced datasets

DOI10.1007/s10994-020-05913-4OpenAlexW3100774601MaRDI QIDQ2051243

Markus Wolfien, Mariam Nassar, Olaf Wolkenhauer, Narek Davtyan, Saptarshi Bej

Publication date: 24 November 2021

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1908.08346

zbMATH Keywords

oversamplingdata augmentationmanifold learningimbalanced datasetssynthetic sample generation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Switching: understanding the class-reversed sampling in tail sample memorization

Uses Software

• SMOTEBoost
• Imbalanced-learn
• t-SNE
• MWMOTE
• SMOTE

Cites Work

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• Noisy replication in skewed binary classification.
• Manifold-based synthetic oversampling with manifold conformance estimation
• Bayesian Analysis for the Social Sciences

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