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Data-Driven Robust Optimization Neural Network Method for Imbalanced Data Classification
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DOI：10.1109/TKDE.2026.3677175.png)
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Abstract
En 中文
Imbalanced data classification is a hot topic in neural network learning. Current neural network methods rely on reweighting and resampling technology to solve the imbalanced data problem. Showing inconsistent rebalancing behavior and easily leading to the learning performance fluctuation in diversified imbalanced data distributions. Robust optimization is a promising direction to alleviate these problems by enhancing the robustness of neural networks. However, designing an uncertain set for robust optimization is an open problem in the imbalance learning domain. In this paper, we develop a novel data-driven robust optimization neural network method for imbalanced data classification. Specifically, we design a more aggressive local span space to generate new minority samples of the uncertain set, providing the maximum uncertain set for minority rebalancing with the robustness enhancement property. Here, we perform the robust optimization with a more stable initial state by pre-training a classification model. Then, we replace the distribution discrepancy constraint with a novel model evaluation constraint to preserve the local details of data distribution. The generated samples of the maximum uncertain set are further applied to fine-tune the delay robustness enhancement neural network, achieving the local data perturbation adaptation with empirical risk minimization. We validated the proposed method on multiple imbalanced data sets with varied imbalance configurations. The test results showed that the proposed method performed better than the state-of-the-art rebalancing methods, revealing that the robustness enhancement is an important factor in improving the stability of imbalanced learning.
Keywords:
Imbalanced data classification
neural network
data-driven robust optimization
maximum uncertain set
learning performance fluctuation
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