AINeutralarXiv – CS AI · 3h ago5/10
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Improving Requirements Classification with SMOTE-Tomek Preprocessing
Researchers applied SMOTE-Tomek preprocessing to address class imbalance in requirements engineering classification, achieving 76.16% accuracy with logistic regression compared to a 58.31% baseline. The technique combines synthetic minority oversampling with Tomek link removal and stratified K-fold validation on the PROMISE dataset of 969 categorized requirements.