用粒计算提升最小二乘孪生SVM的鲁棒性与效率
Enhancing Robustness and Efficiency of Least Square Twin SVM via Granular Computing
- 用粒球替代原始数据点，封装局部信息
- 在多个数据集上优于基线模型，且计算更高效
- 适合处理噪声数据的大规模分类任务
在机器学习领域，最小二乘孪生支持向量机（LSTSVM）是当前先进模型之一。然而，其对噪声和异常值敏感，忽视结构风险最小化（SRM）原则，且在重采样中不稳定。此外，计算复杂度高及依赖矩阵求逆限制了其在大规模数据上的应用。为此，本文提出稳健的粒球最小二乘孪生SVM（GBLSTSVM），使用粒球而非原始数据点进行训练。粒球中心包含球内所有数据点的关键信息。为进一步提升可扩展性与效率，引入大规模GBLSTSVM（LS-GBLSTSVM），通过正则化项融入SRM原则。在UCI、KEEL和NDC基准数据集上的实验表明，所提的GBLSTSVM与LS-GBLSTSVM模型均持续优于基线模型。
原文摘要 · Abstract (English)
In the domain of machine learning, least square twin support vector machine (LSTSVM) stands out as one of the state-of-the-art models. However, LSTSVM suffers from sensitivity to noise and outliers, overlooking the SRM principle and instability in resampling. Moreover, its computational complexity and reliance on matrix inversions hinder the efficient processing of large datasets. As a remedy to the aforementioned challenges, we propose the robust granular ball LSTSVM (GBLSTSVM). GBLSTSVM is trained using granular balls instead of original data points. The core of a granular ball is found at its center, where it encapsulates all the pertinent information of the data points within the ball of specified radius. To improve scalability and efficiency, we further introduce the large-scale GBLSTSVM (LS-GBLSTSVM), which incorporates the SRM principle through regularization terms. Experiments are performed on UCI, KEEL, and NDC benchmark datasets; both the proposed GBLSTSVM and LS-GBLSTSVM models consistently outperform the baseline models.
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