Mixed Integer Linear Programming for Feature Selection in Support Vector Machine

2018/08/07 by Martine Labbé, Labbé, Martine, Luisa I. Martínez-Merino +3 · 3 citations
Computer Science · Decision Sciences · #90C11 #FOS: Computer and information sciences #FOS: Mathematics #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Metaheuristic Optimization Algorithms Research #Multi-Criteria Decision Making #Optimization and Control (math.OC)

paper · pdf · doi:10.48550/arxiv.1808.02435

openalex publication_date 2018/08/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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