# Robust Universum Twin Support Vector Machine for Imbalanced Data

arXiv: https://arxiv.org/abs/2410.20335 · HTML: https://mlsanity.com/paper/2410.20335/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 27 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [Enhancing Multiview Synergy: Robust Learning by Exploiting the Wave Loss Function with Consensus and Complementarity Principles](https://mlsanity.com/paper/2408.06819/) (2024)
- [Intuitionistic Fuzzy Generalized Eigenvalue Proximal Support Vector Machine](https://mlsanity.com/paper/2408.01713/) (2024)
- [Granular Ball Twin Support Vector Machine](https://mlsanity.com/paper/2410.04774/) (2024)
- [Multiview learning with twin parametric margin SVM](https://mlsanity.com/paper/2408.01981/) (2024)
- [Flexi-Fuzz least squares SVM for Alzheimer's diagnosis: Tackling noise, outliers, and class imbalance](https://mlsanity.com/paper/2410.14207/) (2024)

## Built on by (most cited first, 4 of 4)

- [A Unified Framework for EEG Seizure Detection Using Universum-Integrated Generalized Eigenvalues Proximal Support Vector Machine](https://mlsanity.com/paper/2512.21170/) (2025)
- [Graph-Guided Universum Learning in Generalized Eigenvalue Proximal SVMs for Alzheimer's Disease Classification](https://mlsanity.com/paper/2606.04699/) (2026)
- [Robust Dual-Model Collaborative Random Vector Functional Link Network](https://mlsanity.com/paper/2608.13628/) (2026)
- [RoBell-RVFL: A Robust Generalized Bell Random Vector Functional Link Network](https://mlsanity.com/paper/2608.16965/) (2026)

## Abstract (arXiv)

One of the major difficulties in machine learning methods is categorizing datasets that are imbalanced. This problem may lead to biased models, where the training process is dominated by the majority class, resulting in inadequate representation of the minority class. Universum twin support vector machine (UTSVM) produces a biased model towards the majority class, as a result, its performance on the minority class is often poor as it might be mistakenly classified as noise. Moreover, UTSVM is not proficient in handling datasets that contain outliers and noises. Inspired by the concept of incorporating prior information about the data and employing an intuitionistic fuzzy membership scheme, we propose intuitionistic fuzzy UTSVM for imbalanced data (IFUTSVM-ID) by enhancing overall robustness. We use an intuitionistic fuzzy membership scheme to mitigate the impact of noise and outliers. Moreover, to tackle the problem of imbalanced class distribution, data oversampling and undersampling methods are utilized. Prior knowledge about the data is provided by universum data. This leads to better generalization performance. UTSVM is susceptible to overfitting risks due to the omission of the structural risk minimization (SRM) principle in their primal formulations. However, the proposed IFUTSVM-ID model incorporates the SRM principle through the incorporation of regularization terms, effectively addressing the issue of overfitting. We conduct a comprehensive evaluation of the proposed IFUTSVM-ID model on benchmark datasets from KEEL and compare it with existing baseline models. Furthermore, to assess the effectiveness of the proposed IFUTSVM-ID model in diagnosing Alzheimer's disease (AD), we applied them to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results showcase the superiority of the proposed IFUTSVM-ID models compared to the baseline models.