Mixed Integer Linear Programming for Feature Selection in Support Vector Machine
This work addresses feature selection for SVM users, but it is incremental as it applies existing MILP techniques to a known problem without broad new insights.
The authors tackled feature selection in Support Vector Machines by proposing a Mixed Integer Linear Programming formulation with a budget constraint to limit features, and they developed exact and heuristic solution methods, validating the approach on well-known datasets against classical methods.
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.