Balanced Random Hyperboxes for Class Imbalanced Problems
- Publisher:
- IAENG - International Association of Engineers
- Publication Type:
- Journal Article
- Citation:
- IAENG International Journal of Computer Science, 2021, 48, (2), pp. 406-412
- Issue Date:
- 2021-01-01
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A Random Hyperboxes (RH) classifier is a simple but powerful randomization-based ensemble model, including hyperbox-based classifiers used as base learners. Individual learners in this ensemble model are trained on random subspaces of both instance and feature spaces. This facet results in a flexible mechanism to form a high-performing classifier competitive with other ensemble models in the literature. Like other machine learning models, however, the RH classifier also faces inefficiency when dealing with class-imbalanced datasets. Meanwhile, data containing highly imbalanced class distributions are prevalent in practical applications. Hence, this paper proposes a new variant of the original RH model, namely Balance Random Hyperboxes (BRH), to bypass this drawback effectively. The proposed method uses an undersampling strategy to build individual learners instead of the random sampling method employed in the original RH model. The experiment conducted on software fault datasets, which show a highly class-imbalanced property, indicated the proposed method's efficiency compared to the original RH model and other ensemble models.
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