# Robust Support Vector Machines for Imbalanced and Noisy Data via Benders Decomposition

arXiv: https://arxiv.org/abs/2503.14873 · HTML: https://mlsanity.com/paper/2503.14873/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 19 Mar 2025.

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

- [How Complex is your classification problem? A survey on measuring classification complexity](https://mlsanity.com/paper/1808.03591/) (2018)
- [Machine Learning for Microcontroller-Class Hardware: A Review](https://mlsanity.com/paper/2205.14550/) (2022)
- [A Novel Intrinsic Measure of Data Separability](https://mlsanity.com/paper/2109.05180/) (2021)
- [GL-TSVM: A robust and smooth twin support vector machine with guardian loss function](https://mlsanity.com/paper/2408.16336/) (2024)
- [Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach](https://mlsanity.com/paper/2411.17128/) (2024)

## Built on by (most cited first, 2 of 2)

- [MicroNAS: An Automated Framework for Developing a Fall Detection System](https://mlsanity.com/paper/2504.07397/) (2025)
- [Personalized Control for Lower Limb Prosthesis Using Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2505.09366/) (2025)

## Abstract (arXiv)

This study introduces a novel formulation to enhance Support Vector Machines (SVMs) in handling class imbalance and noise. Unlike the conventional Soft Margin SVM, which penalizes the magnitude of constraint violations, the proposed model quantifies the number of violations and aims to minimize their frequency. To achieve this, a binary variable is incorporated into the objective function of the primal SVM formulation, replacing the traditional slack variable. Furthermore, each misclassified sample is assigned a priority and an associated constraint. The resulting formulation is a mixed-integer programming model, efficiently solved using Benders decomposition. The proposed model's performance was benchmarked against existing models, including Soft Margin SVM, weighted SVM, and NuSVC. Two primary hypotheses were examined: 1) The proposed model improves the F1-score for the minority class in imbalanced classification tasks. 2) The proposed model enhances classification accuracy in noisy datasets. These hypotheses were evaluated using a Wilcoxon test across multiple publicly available datasets from the OpenML repository. The results supported both hypotheses (( p < 0.05 )). In addition, the proposed model exhibited several interesting properties, such as improved robustness to noise, a decision boundary shift favoring the minority class, a reduced number of support vectors, and decreased prediction time. The open-source Python implementation of the proposed SVM model is available.