Granular Ball Twin Support Vector Machine with Universum Data

Innovative Data Representation with Granular Balls: The GBU-TSVM model employs an innovative approach by representing data instances as granular balls rather than conventional points. This method improves the model's robustness and efficiency, especially in handling noisy and large datasets. By grouping data points into granular balls, the model achieves better computational efficiency, increased noise resistance, and enhanced interpretability, establishing a new standard in data representation. Enhanced Generalization using Universum Data: The GBU-TSVM incorporates Universum data, which includes samples outside the target classes, to significantly improve generalization capabilities. Universum data enables the classifier to perform better on benchmark datasets, demonstrating the model's ability to utilize additional knowledge for more precise predictions. Refined Learning with Modified Hinge Loss Function: The model includes an advanced hinge loss function that accounts for the radii of granular balls, leading to a more accurate error measure and learning process. This modification allows for a detailed error assessment, enhancing the model's learning efficiency and decision boundary precision. By addressing the limitations of existing TSVM models, this innovation sets a new benchmark in the field of machine learning classifiers.