Mixed integer linear programming for feature selection in support vector machine
This work focuses on support vector machine (SVM) with feature selection. A MILP formula- tion is proposed for the problem. The choice of suitable features to construct the separating hyperplanes has been modeled in this formulation by including a budget constraint that sets in advance a limit on th...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/138253 |
| Acceso en línea: | https://hdl.handle.net/11441/138253 https://doi.org/10.1016/j.dam.2018.10.025 |
| Access Level: | acceso abierto |
| Palabra clave: | Mathematical programming Kernel search algorithm Supervised classification Support vector machine Feature selection |
| Sumario: | This work focuses on support vector machine (SVM) with feature selection. A MILP formula- tion is proposed for the problem. The choice of suitable features to construct the separating hyperplanes has been modeled 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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