Synthetic minority augmentation improves threshold-integrated and optimized classification metrics only under model misspecification by correcting ranking errors, while providing no fundamental benefit beyond possible variance reduction under well-specified score models.
arXiv preprint arXiv:2406.03628 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
First stance detection study on prediction market commentary finds market context raises 3-class Anti recall from 0.10 to 0.45 while 50% counterfactual augmentation is optimal and full augmentation hurts performance.
A Gaussian mixture MIL framework with partially subsampled instances improves metastasis prediction accuracy on breast cancer whole-slide images over prior methods.
citing papers explorer
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When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?
Synthetic minority augmentation improves threshold-integrated and optimized classification metrics only under model misspecification by correcting ranking errors, while providing no fundamental benefit beyond possible variance reduction under well-specified score models.
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Stance Detection in Prediction Markets: Addressing Imbalanced Trader Commentary via Counterfactual Augmentation and Market Context
First stance detection study on prediction market commentary finds market context raises 3-class Anti recall from 0.10 to 0.45 while 50% counterfactual augmentation is optimal and full augmentation hurts performance.
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Detecting Breast Carcinoma Metastasis on Whole-Slide Images by Partially Subsampled Multiple Instance Learning
A Gaussian mixture MIL framework with partially subsampled instances improves metastasis prediction accuracy on breast cancer whole-slide images over prior methods.