2023/04/20 by Gabriel O. Assunção, Assunção, Gabriel O., Rafael Izbicki +3
Computer Science · Engineering · #Computation and Language (cs.CL) #Electricity Theft Detection Techniques #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning (stat.ML) #Text and Document Classification Technologies
paper · pdf · doi:10.48550/arxiv.2304.10283
openalex publication_date 2023/04/20 · openalex created_date 2023/04/24 · openalex updated_date 2026/08/01
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.