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A unified optimization framework for multiclass classification with structured hyperplane arrangements

Víctor Blanco et al.

Computational Optimization and Applications2026https://doi.org/10.1007/s10589-026-00779-zarticle
AJG 3
Weight
0.50

What the paper says

In this paper, we propose a new mathematical optimization model for multiclass classification based on arrangements of hyperplanes. Our approach preserves the core support vector machine (SVM) paradigm of maximizing class separation while minimizing misclassification errors, and it is computationally more efficient than a previous formulation. We present a kernel-based extension that allows it to construct nonlinear decision boundaries. Furthermore, we show how the framework can naturally incorporate alternative geometric structures, including classification trees, $$\ell _p$$ ℓ p -SVMs, and models with discrete feature selection. To address large-scale instances, we develop a dynamic clustering matheuristic that leverages the proposed MIP formulation. Extensive computational experiments demonstrate the efficiency of the proposed model and dynamic clustering heuristic, and we report competitive classification performance on both synthetic datasets and real-world benchmarks from the UCI machine learning repository, comparing our method with state-of-the-art implementations available in .

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https://doi.org/https://doi.org/10.1007/s10589-026-00779-z

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@article{víctor2026,
  title        = {{A unified optimization framework for multiclass classification with structured hyperplane arrangements}},
  author       = {Víctor Blanco et al.},
  journal      = {Computational Optimization and Applications},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s10589-026-00779-z},
}

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A unified optimization framework for multiclass classification with structured hyperplane arrangements

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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