Granular Ball Twin Support Vector Machine with Universum Data

TL;DR

The paper introduces GBU-TSVM, a robust and efficient classification model that leverages granular ball computing and Universum data to improve accuracy and noise resistance over traditional TSVM methods.

Contribution

It proposes a novel GBU-TSVM model that uses hyper-balls for data representation and incorporates Universum data, enhancing robustness and efficiency in classification tasks.

Findings

Outperforms existing TSVM models in accuracy.

Demonstrates improved computational efficiency.

Shows increased robustness to noise and outliers.

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.