用颗粒球简化数据，让支持向量回归更快更抗噪。
GBSVR: Granular Ball Support Vector Regression
- 用颗粒球聚类数据点，减少计算量
- 在多个数据集上优于现有先进方法
- 适合处理大规模、含异常值的数据
支持向量回归（SVR）及其变体广泛用于回归任务，但由于需求解高成本的二次规划问题，限制了其在大规模数据上的应用。此外，SVR采用ε不敏感损失函数，对异常值敏感，影响性能。本文提出颗粒球支持向量回归（GBSVR），引入颗粒球概念，将数据点按距离分组为颗粒球，从而降低计算复杂度。该方法还提出连续属性离散化方法以支持颗粒球构建。在多个基准数据集上的实验表明，该方法性能优于现有先进方法。
原文摘要 · Abstract (English)
Support Vector Regression (SVR) and its variants are widely used to handle regression tasks, however, since their solution involves solving an expensive quadratic programming problem, it limits its application, especially when dealing with large datasets. Additionally, SVR uses an epsilon-insensitive loss function which is sensitive to outliers and therefore can adversely affect its performance. We propose Granular Ball Support Vector Regression (GBSVR) to tackle problem of regression by using granular ball concept. These balls are useful in simplifying complex data spaces for machine learning tasks, however, to the best of our knowledge, they have not been sufficiently explored for regression problems. Granular balls group the data points into balls based on their proximity and reduce the computational cost in SVR by replacing the large number of data points with far fewer granular balls. This work also suggests a discretization method for continuous-valued attributes to facilitate the construction of granular balls. The effectiveness of the proposed approach is evaluated on several benchmark datasets and it outperforms existing state-of-the-art approaches
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