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<p>By taking the data sampling information and distribution information into account, we establish an enhanced support vector machine (SVM) optimization model for binary classification problems based on minimizing the worst-case misclassification probability and the number of misclassified samples in this paper. Since deeper data structure information is employed, the proposed model is more robust. With the aid of the projection penalty and the alternating minimization technique, we design a numerical solution method for solving the model. The efficiency and performance of the proposed model and the solution method are validated via theoretical analysis as well as the numerical experiments performed on the benchmark and synthetic datasets.</p>

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An Enhanced SVM Model Based on Data Sampling and Distribution

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Jia-Kang Du,
  • Chun-Yan Wang,
  • Yuan-Hai Shao,
  • Yi-Ju Wang

摘要

By taking the data sampling information and distribution information into account, we establish an enhanced support vector machine (SVM) optimization model for binary classification problems based on minimizing the worst-case misclassification probability and the number of misclassified samples in this paper. Since deeper data structure information is employed, the proposed model is more robust. With the aid of the projection penalty and the alternating minimization technique, we design a numerical solution method for solving the model. The efficiency and performance of the proposed model and the solution method are validated via theoretical analysis as well as the numerical experiments performed on the benchmark and synthetic datasets.