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<p>Support vector machines (SVMs) are powerful supervised learning methods widely used for classification and regression. However, theirs performance can significantly deteriorate in the presence of label noise and class imbalance, which are common in real-world applications. To address these issues, we propose a novel asymmetric bounded loss function within the general framework of robust loss functions for machine learning (RML). This loss function allows for class-dependent misclassification penalties while ensuring robustness to label noise. By incorporating this loss into the SVM framework, we develop a new model, referred to as asymmetric loss SVM (ALSVM), which is efficiently optimized via the stochastic variance reduced gradient method. We further provide a theoretical analysis by deriving generalization error bounds based on Rademacher complexity, and demonstrate the robustness by showing that its influence function is bounded. Extensive experiments on benchmark datasets, particularly those related to disease classification, demonstrate that the proposed method consistently improves performance across multiple evaluation metrics.</p>

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An RML-Based Asymmetric Loss Support Vector Machine for Robust and Imbalanced Classification

  • 期刊论文

  • 发表日期：

    2026年1月18日

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  • Yu-Nuo Chen,
  • Kai Qi,
  • Li-Ping Tang

摘要

Support vector machines (SVMs) are powerful supervised learning methods widely used for classification and regression. However, theirs performance can significantly deteriorate in the presence of label noise and class imbalance, which are common in real-world applications. To address these issues, we propose a novel asymmetric bounded loss function within the general framework of robust loss functions for machine learning (RML). This loss function allows for class-dependent misclassification penalties while ensuring robustness to label noise. By incorporating this loss into the SVM framework, we develop a new model, referred to as asymmetric loss SVM (ALSVM), which is efficiently optimized via the stochastic variance reduced gradient method. We further provide a theoretical analysis by deriving generalization error bounds based on Rademacher complexity, and demonstrate the robustness by showing that its influence function is bounded. Extensive experiments on benchmark datasets, particularly those related to disease classification, demonstrate that the proposed method consistently improves performance across multiple evaluation metrics.