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Robust Support Vector Machines for Imbalanced and Noisy Data via Benders Decomposition by Mohasel, Seyed Mojtaba; Koosha, Hamidreza is a scholarly article available to read on EtoBox.

What is Robust Support Vector Machines for Imbalanced and Noisy Data via Benders Decomposition about?

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 addit

Author

Mohasel, Seyed Mojtaba; Koosha, Hamidreza

Published

2025

Language

EN