SMOTE

A data augmentation technique that addresses class imbalance in machine learning datasets by generating synthetic examples of the minority class rather than simply duplicating existing ones. SMOTE creates new instances by interpolating between existing minority class samples and their nearest neighbors. This technique is relevant to accessibility research because accessibility-related data (such as bug reports, user reviews, or compliance violations) is often vastly outnumbered by non-accessibility data, creating the class imbalance that SMOTE helps correct.