| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000607913 |
| ISSN: | 1405-5546 |
| Autores: | Medjahed, Seyyid Ahmed1 Boukhatem, Fatima2 |
| Instituciones: | 1University of Relizane, Argelia 2University Djillali Liabes, Argelia |
| Año: | 2024 |
| Periodo: | Abr-Jun |
| Volumen: | 28 |
| Número: | 2 |
| Paginación: | 607-622 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | In many supervised learning problems, feature selection techniques are increasingly essential across various applications. Feature selection significantly influences the classification accuracy rate and the quality of SVM model by reducing the number of features, remove irrelevant and redundant features. In this paper, we evaluate the performance of twenty feature selection algorithms over four databases. The performance is conducted in term of: classification accuracy rate, Kuncheva’s Stability, Information Stability, SS Stability and SH Stability. To measure the feature selection algorithms, multiple datasets from the UCI Machine Learning Repository are utilized to assess both classification accuracy and stability variations. |
| Keyword: | Feature selection, Classification, Stability, Support vector machine |
| Texto completo: | Texto completo (Ver PDF) Texto completo (Ver HTML) |
