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A comprehensive evaluation of oversampling techniques for enhancing text classification performance.

Salimkan Fatma Taskiran1, Bahaeddin Turkoglu2, Ersin Kaya1

  • 1Department of Computer Engineering, Konya Technical University, Konya, 42250, Turkey.

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|July 2, 2025
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Summary

Class imbalance in text classification hinders model performance. This study benchmarks Synthetic Minority Over-sampling Technique (SMOTE) and its variants on transformer-embedded data, offering insights for robust natural language processing.

Keywords:
Imbalanced datasetsSynthetic minority over-sampling technique (SMOTE)Text classification

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Data Science

Background:

  • Class imbalance is a critical challenge in text classification, impairing model learning of minority classes.
  • Skewed data distributions, following the 'garbage in, garbage out' principle, can lead to suboptimal performance even with advanced models.

Purpose of the Study:

  • To systematically benchmark the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) and its 30 variants.
  • To evaluate these oversampling methods in the context of transformer-embedded text classification.
  • To provide practical guidance for selecting appropriate oversampling techniques for imbalanced datasets.

Main Methods:

  • Utilized two benchmark datasets: TREC and Emotions.
  • Employed the MiniLMv2 transformer model for semantic text vectorization.
  • Applied six distinct machine learning algorithms for classification tasks.
  • Compared performance using F1-Score and Balanced Accuracy under balanced and imbalanced scenarios.
  • Validated results using the Friedman test for statistical significance.

Main Results:

  • Demonstrated significant performance variations among different SMOTE variants across datasets and classifiers.
  • Identified specific SMOTE techniques that effectively mitigate the negative impact of class imbalance.
  • Provided empirical evidence on the suitability of oversampling methods for transformer-based text classification.

Conclusions:

  • The choice of SMOTE variant significantly impacts the performance of text classifiers on imbalanced data.
  • Transformer embeddings combined with appropriate oversampling techniques can lead to more robust and fair NLP models.
  • This large-scale benchmarking offers practical insights for practitioners dealing with imbalanced text classification challenges.