| Revue: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560152 |
| ISSN: | 1405-5546 |
| Autores: | Medjahed, Seyyid Ahmed1 Ouali, Mohammed2 |
| Instituciones: | 1Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf, Oran. Argelia 2Taif University, College of Computers and Information Technology, Taif. Arabia Saudita |
| Año: | 2018 |
| Periodo: | Abr-Jun |
| Volumen: | 22 |
| Número: | 2 |
| Paginación: | 675-683 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Microarray expression data has been a very active research field and an indispensable tool for cancer diagnosis. The microarray expression dataset contains thousands of genes and selecting a subset of informative genes is a primordial preprocessing step for improving the cancer classification. Support Vector Machine Recursive Feature Elimination (SVM-RFE) is one of the popular and effective gene selection approaches. However, SVM-RFE attempts to find the best possible combination for classification and does not take into account the ability of class separability for each gene. In this paper, a novel SVM-RFE based on energy distance (ED) and called SVM-RFE-ED is proposed to overcome the limitation of standard SVM-RFE. The aims of our study are to achieve a high classification accuracy rate and improve the classification model. The experimentation is conducted on five widely used datasets. Experimental results indicate that the proposed approach SVM-RFE-ED provides good results and achieve a high classification accuracy rate using a small number of genes. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Cancer diagnosis, Support vector machine, Recursive feature elimination, Gene selection, Energy distance, Classification, Artificial intelligence |
| Texte intégral: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
