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...
| Authors: | , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2019 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/138253 |
| Online Access: | https://hdl.handle.net/11441/138253 https://doi.org/10.1016/j.dam.2018.10.025 |
| Access Level: | Open access |
| Keyword: | Mathematical programming Kernel search algorithm Supervised classification Support vector machine Feature selection |
| Summary: | 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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