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Maulana Ihsan Ahmad
Universitas Gadjah Mada

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Evaluating oversampling methods for imbalanced Arabic dialect identification Maulana Ihsan Ahmad; Aina Musdholifah; Arif Nurwidyantoro
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp259-270

Abstract

This study investigates whether oversampling is a reliable solution for severe class imbalance in Arabic dialect identification. Using the Shami Corpus as a controlled testbed, we demonstrate that conventional oversampling often fails in high-dimensional sparse text spaces, but density based cluster filtering can effectively resolve this. We conduct a comparative evaluation of SMOTE, clustering-guided variants (ASTRA-SMOTE and SMOTE-RADIANT), and a cost-sensitive ClassWeight approach under an identical 5,644-dimensional feature-engineering pipeline using LightGBM and XGBoost. On the held-out test set, standard SMOTE and class weighting frequently distorted decision boundaries, yielding inconsistent gains across models. In contrast, SMOTE-RADIANT yields a statistically significant macro-F1 improvement for LightGBM (0.8539 vs. 0.8526 on the original data) with a large effect size (r = 0.511), successfully rescuing minority dialects without degrading the majority class. These findings suggest that while oversampling is not universally reliable in sparse text spaces, coupling it with density-based noise neutralization (RADIANT) provides a robust and interpretable alternative to deep learning models. This study provides methodological clarity and reproducible guidance for fair and inclusive Arabic NLP systems.
Co-Authors Aina Musdholifah Arif Nurwidyantoro