新分类器融合粒度球与双支持向量机，提升多分类精度与效率。
Granular Ball K-Class Twin Support Vector Classifier
- 用粒度球表示数据，增强抗噪能力；采用非平行超平面结构降低计算复杂度。
- 在多个基准数据集上准确率和速度均优于现有先进方法。
- 适合需要高鲁棒性与高效性的模式识别、故障诊断等场景。
本文提出一种新型多分类框架——粒度球K类双支持向量分类器（GB-TWKSVC），将双支持向量机（TWSVM）与粒度球计算相结合。该方法通过粒度球表示提升对噪声的鲁棒性，利用TWSVM的非平行超平面架构，解决两个更小的二次规划问题，从而提高计算效率。所提方法引入新公式，有效处理多分类任务，超越传统二分类方法。在多个基准数据集上的实验表明，GB-TWKSVC在准确率和计算性能方面显著优于当前最先进的分类器。通过全面的统计检验和复杂度分析验证了其有效性。本工作为现代机器学习应用提供了数学严谨的可扩展、高鲁棒性分类框架，在模式识别、故障诊断和大规模数据分析等领域具有广泛应用前景。
原文摘要 · Abstract (English)
This paper introduces the Granular Ball K-Class Twin Support Vector Classifier (GB-TWKSVC), a novel multi-class classification framework that combines Twin Support Vector Machines (TWSVM) with granular ball computing. The proposed method addresses key challenges in multi-class classification by utilizing granular ball representation for improved noise robustness and TWSVM's non-parallel hyperplane architecture solves two smaller quadratic programming problems, enhancing efficiency. Our approach introduces a novel formulation that effectively handles multi-class scenarios, advancing traditional binary classification methods. Experimental evaluation on diverse benchmark datasets shows that GB-TWKSVC significantly outperforms current state-of-the-art classifiers in both accuracy and computational performance. The method's effectiveness is validated through comprehensive statistical tests and complexity analysis. Our work advances classification algorithms by providing a mathematically sound framework that addresses the scalability and robustness needs of modern machine learning applications. The results demonstrate GB-TWKSVC's broad applicability across domains including pattern recognition, fault diagnosis, and large-scale data analytics, establishing it as a valuable addition to the classification algorithm landscape.
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