Revisiting One-Versus-One and One-Versus-Rest: Insights into Imbalanced Multi-Class Classification
Chen, Kuan-Ting Lin, Chih-Jen
Chen, Kuan-Ting
Lin, Chih-Jen
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Abstract
One-versus-one (OVO) and one-versus-rest (OVR) are two widely adopted methods to decompose multi-class problems into several binary classification problems. It is well known that, in the case of kernel SVM, the two methods yield similar test accuracy. Thus, people generally assume that they differ mainly in training time and model size. However, our research reveals that if one considers an evaluation metric taking class imbalance into account, these two methods may give notable performance differences. To explore this phenomenon, we first conduct a detailed analysis of kernel SVM and then extend our study to neural networks. Additionally, we propose novel loss functions for neural networks that effectively integrate the OVO and OVR perspectives. Our experiments clearly demonstrate the robustness of OVO in handling imbalanced multi-class classification, highlighting its advantages over OVR in these challenging scenarios.
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K.-T. Chen, C.-J. Lin, "Revisiting One-Versus-One and One-Versus-Rest: Insights into Imbalanced Multi-Class Classification," 2026, pp. 150-158.
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2025 IEEE International Conference on Data Mining (ICDM)
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2025 IEEE International Conference on Data Mining (ICDM)
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46 Information and Computing Sciences, 4611 Machine Learning
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2025 IEEE International Conference on Data Mining (ICDM)
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IEEE