SMOTE
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Large Language Models for Imbalanced Classification: Diversity makes the difference
arXiv:2510.09783v2 Announce Type: replace Abstract: Oversampling is one of the most widely used approaches for addressing imbalanced classification. The core idea is to generate additional minority samples to rebalance the dataset. Most existing methods, such as SMOTE, require converting categorical variables into numerical vectors, which often leads to information loss.
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