https://doi.org/10.1140/epjp/s13360-025-06783-2
Regular Article
How effective oversampling techniques are in classifying potentially hazardous asteroids
1
Department of Physics, Jagannath University, 9-10, Chittaranjan Avenue, 1100, Dhaka, Bangladesh
2
GIS Division, Center for Environmental and Geographic Information Services, F-14/E, Agargaon Administrative Area, 1207, Dhaka, Bangladesh
Received:
24
March
2025
Accepted:
21
August
2025
Published online:
11
September
2025
Potentially hazardous asteroids require strict surveillance to ensure the safety of our planet. However, the vast amount of increasing astronomical data makes it troublesome for humans to study these asteroids. Hence, machine learning techniques are used to classify these hazardous asteroids. However, machine learning models are not robust for distinguishing imbalanced classes. Various undersampling and oversampling techniques are used to address this problem. In our study, we refrained from using any undersampling technique as we did not want to lose any valuable information. Instead, we employed various oversampling techniques, including random Oversampling, SMOTE (Synthetic minority over-sampling technique), ADASYN (Adaptive Synthetic Sampling), BorderlineSmote, KMeansSmote, and SVMSmote. For each oversampling technique, we trained the Random Forest, XGBoost, LightGBM, HistGradientBoosting, and AdaBoost classifiers. Our research presents a detailed study of these oversampling techniques to determine which one is more suitable for our dataset.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
