Robust and efficient learning with granular ball support vector regression

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 their applicability in several real life applications. Additionally, SVR uses an ϵ-insensitive loss function which is sensitive to noise and 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 such as classification and clustering, however, to the best of our knowledge, they have not been sufficiently explored for regression problems. Granular balls generate a coarse and noise tolerant data representation by grouping the data points into balls based on their feature proximity, and reduce the computational cost in SVR by substituting multiple data points with far fewer granular balls. To handle regression scenario, 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. All the codes has been released in the open source at Github Code.