Manifold-based synthetic oversampling with manifold conformance estimation

DOI10.1007/S10994-017-5670-4zbMath1457.68227DBLPjournals/ml/BellingerDJ18OpenAlexW2765177294WikidataQ62810006 ScholiaQ62810006MaRDI QIDQ1640402

Nathalie Japkowicz, Christopher Drummond, Colin Bellinger

Publication date: 14 June 2018

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

Full work available at URL: https://doi.org/10.1007/s10994-017-5670-4

zbMATH Keywords

manifold learningclass imbalanceSMOTEsynthetic oversampling

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Statistics on manifolds (62R30) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

Matrix sketching for supervised classification with imbalanced classes ⋮ Manifold-based synthetic oversampling with manifold conformance estimation ⋮ Switching: understanding the class-reversed sampling in tail sample memorization ⋮ LoRAS: an oversampling approach for imbalanced datasets ⋮ RB-CCR: radial-based combined cleaning and resampling algorithm for imbalanced data classification

Uses Software

• psych
• CIFAR
• SMOTE
• ADASYN

Cites Work

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• Topological Properties of Manifolds
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Machine Learning: ECML 2004
• Supervised versus unsupervised binary-learning by feedforward neural networks

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