# Mixed Integer Linear Programming for Feature Selection in Support Vector Machine

arXiv: https://arxiv.org/abs/1808.02435 · HTML: https://mlsanity.com/paper/1808.02435/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [Margin Optimal Classification Trees](https://mlsanity.com/paper/2210.10567/) (2022)
- [Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective](https://mlsanity.com/paper/2310.05464/) (2023)
- [Support Vector Machines with the Hard-Margin Loss: Optimal Training via Combinatorial Benders' Cuts](https://mlsanity.com/paper/2207.07690/) (2022)
- [Feature subset selection for kernel SVM classification via mixed-integer optimization](https://mlsanity.com/paper/2205.14325/) (2022)
- [A Unified Optimization Framework for Multiclass Classification with Structured Hyperplane Arrangements](https://mlsanity.com/paper/2510.05047/) (2025)

## Abstract (arXiv)

This work focuses on support vector machine (SVM) with feature selection. A MILP formulation is proposed for the problem. The choice of suitable features to construct the separating hyperplanes has been modelled in this formulation by including a budget constraint that sets in advance a limit on the number of features to be used in the classification process. We propose both an exact and a heuristic procedure to solve this formulation in an efficient way. Finally, the validation of the model is done by checking it with some well-known data sets and comparing it with classical classification methods.