Is augmentation effective to improve prediction in imbalanced text datasets?
Imbalanced datasets present a significant challenge for machine learning
models, often leading to biased predictions. To address this issue, data
augmentation techniques are widely used in natural language processing (NLP) to
generate new samples for the minority class. However, in this paper, we
challenge the common assumption that data augmentation is always necessary to
improve predictions on imbalanced datasets. Instead, we argue that adjusting
the classifier cutoffs without data augmentation can produce similar results to
oversampling techniques. Our study provides theoretical and empirical evidence
to support this claim. Our findings contribute to a better understanding of the
strengths and limitations of different approaches to dealing with imbalanced
data, and help researchers and practitioners make informed decisions about
which methods to use for a given task.