| Título |
Evolutionary learning using a sensitivity-accuracy approach for classification |
| Autores |
SÁNCHEZ MONEDERO, JAVIER, Hervás-Martínez C. , MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, CARBONERO RUZ, MARIANO, Moreno M.C.R. , Cruz-Ramírez M. |
| Publicación externa |
No |
| Medio |
Lect. Notes Comput. Sci. |
| Alcance |
Conference Paper |
| Naturaleza |
Científica |
| Cuartil JCR |
4 |
| Cuartil SJR |
2 |
| Impacto SJR |
0.32200 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954603059&doi=10.1007%2f978-3-642-13803-4_36&partnerID=40&md5=147a8426ccabc6839d8d6047aa62c119 |
| Fecha de publicacion |
01/01/2010 |
| Scopus Id |
2-s2.0-77954603059 |
| DOI |
10.1007/978-3-642-13803-4_36 |
| Abstract |
Accuracy alone is insufficient to evaluate the performance of a classifier especially when the number of classes increases. This paper proposes an approach to deal with multi-class problems based on Accuracy (C) and Sensitivity (S). We use the differential evolution algorithm and the ELM-algorithm (Extreme Learning Machine) to obtain multi-classifiers with a high classification rate level in the global dataset with an acceptable level of accuracy for each class. This methodology is applied to solve four benchmark classification problems and obtains promising results. © 2010 Springer-Verlag. |
| Palabras clave |
Benchmark classification; Classification rates; Data sets; Differential evolution algorithms; Evolutionary Learning; Extreme learning machine; Multi-class problems; Artificial intelligence; Classifica |
| Miembros de la Universidad Loyola |
|
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