Granular Ball Twin Support Vector Machine with Universum Data
M. A. Ganaie, Vrushank Ahire
TL;DR
GBU-TSVM advances binary classification by marrying granular-ball computing with Universum data within a Twin SVM framework, enabling robust handling of noise and outliers while improving computational efficiency. It provides linear and nonlinear (RBF kernel) formulations, deriving optimized hyperplanes that account for granular-ball geometry and Universum samples. Experimental results on diverse UCI benchmarks demonstrate superior accuracy and faster training compared to TSVM and U-TSVM variants, with strong statistical validation. The approach offers a scalable, context-aware classification model with practical impact on imbalanced and noisy datasets.
Abstract
Classification with support vector machines (SVM) often suffers from limited performance when relying solely on labeled data from target classes and is sensitive to noise and outliers. Incorporating prior knowledge from Universum data and more robust data representations can enhance accuracy and efficiency. Motivated by these findings, we propose a novel Granular Ball Twin Support Vector Machine with Universum Data (GBU-TSVM) that extends the TSVM framework to leverage both Universum samples and granular ball computing during model training. Unlike existing TSVM methods, the proposed GBU-TSVM represents data instances as hyper-balls rather than points in the feature space. This innovative approach improves the model's robustness and efficiency, particularly in handling noisy and large datasets. By grouping data points into granular balls, the model achieves superior computational efficiency, increased noise resistance, and enhanced interpretability. Additionally, the inclusion of Universum data, which consists of samples that are not strictly from the target classes, further refines the classification boundaries. This integration enriches the model with contextual information, refining classification boundaries and boosting overall accuracy. Experimental results on UCI benchmark datasets demonstrate that the GBU-TSVM outperforms existing TSVM models in both accuracy and computational efficiency. These findings highlight the potential of the GBU-TSVM model in setting a new standard in data representation and classification.