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An Innovative Oversampling Approach Using Functional Data Analysis for Imbalanced Classification

https://doi.org/10.2139/ssrn.5257773
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An Introduction to Functional Data Analysis
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Handling Class-Imbalance with KNN (Neighbourhood) Under-Sampling for Software Defect Prediction
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Mammographic Classification Using Stacked Ensemble Learning with Bagging and Boosting Techniques
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MLTL: A multi-label approach for the Tomek Link undersampling algorithm
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Sample-pair learning network for extremely imbalanced classification
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FW-SMOTE: A feature-weighted oversampling approach for imbalanced classification
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Noise-robust oversampling for imbalanced data classification
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Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification
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Implementation of ensemble machine learning classifiers to predict diarrhoea with SMOTEENN, SMOTE, and SMOTETomek class imbalance approaches
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Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN)
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Attack Classification of Imbalanced Intrusion Data for IoT Network Using Ensemble-Learning-Based Deep Neural Network
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NUS: Noisy-Sample-Removed Undersampling Scheme for Imbalanced Classification and Application to Credit Card Fraud Detection
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Imbalanced Classification Based on Minority Clustering Synthetic Minority Oversampling Technique With Wind Turbine Fault Detection Application
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Granular Ball Sampling for Noisy Label Classification or Imbalanced Classification
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DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data
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Over- and Under-sampling Approach for Extremely Imbalanced and Small Minority Data Problem in Health Record Analysis
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On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests
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Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
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Improved Training of Wasserstein GANs
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Functional Data Analysis: An Introduction and Recent Developments
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