Granular ball twin support vector machine with Universum data

Support vector machines often underperform when limited to labelled target class data and demonstrate sensitivity to noise and outliers. To address these limitations, we propose the Granular Ball Twin Support Vector Machine with Universum Data (GBU-TSVM), which uniquely integrates Universum samples with granular ball computing in the TSVM framework. Unlike conventional TSVMs representing data as points in feature space, the proposed GBU-TSVM models instances as hyperballs, significantly improving robustness against noise while enhancing computational efficiency. Granular representation enables effective data grouping, reducing processing complexity while preserving critical structural information. Incorporating Universum data, consisting of samples outside the target classes, provides additional contextual information that refines decision boundaries and improves generalization. Experiments on UCI benchmark datasets demonstrate GBU-TSVM's superior performance, measured in terms of accuracy and training time. It achieves 92.38 % accuracy on the Molec Biol Promoter dataset under optimal conditions and maintains 89.17 % accuracy even with 20 % noise contamination. It consistently outperforms baseline models such as GBSVM, TSVM, GBTSVM, Pin-GTSVM, and UTSVM. These results establish GBU-TSVM as an advanced framework for robust classification in challenging data environments.