Restoring balance: principled under/oversampling of data for optimal classification

• URL: http://arxiv.org/abs/2405.09535v1
• Date: Wed, 15 May 2024 17:45:34 GMT
• Title: Restoring balance: principled under/oversampling of data for optimal classification
• Authors: Emanuele Loffredo, Mauro Pastore, Simona Cocco, Rémi Monasson,
• Abstract summary: Class imbalance in real-world data poses a common bottleneck for machine learning tasks. Mitigation strategies, such as under or oversampling the data depending on their abundances, are routinely proposed and tested empirically. We provide a sharp prediction of the effects of under/oversampling strategies depending on class imbalance, first and second moments of the data, and the metrics of performance considered.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Class imbalance in real-world data poses a common bottleneck for machine learning tasks, since achieving good generalization on under-represented examples is often challenging. Mitigation strategies, such as under or oversampling the data depending on their abundances, are routinely proposed and tested empirically, but how they should adapt to the data statistics remains poorly understood. In this work, we determine exact analytical expressions of the generalization curves in the high-dimensional regime for linear classifiers (Support Vector Machines). We also provide a sharp prediction of the effects of under/oversampling strategies depending on class imbalance, first and second moments of the data, and the metrics of performance considered. We show that mixed strategies involving under and oversampling of data lead to performance improvement. Through numerical experiments, we show the relevance of our theoretical predictions on real datasets, on deeper architectures and with sampling strategies based on unsupervised probabilistic models.

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