| Título |
Regularized ensemble neural networks models in the Extreme Learning Machine framework |
| Autores |
PERALES GONZÁLEZ, CARLOS, CARBONERO RUZ, MARIANO, BECERRA ALONSO, DAVID, PÉREZ RODRÍGUEZ, JAVIER, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS |
| Publicación externa |
No |
| Medio |
Neurocomputing |
| Alcance |
Article |
| Naturaleza |
Científica |
| Cuartil JCR |
1 |
| Cuartil SJR |
1 |
| Impacto JCR |
4.43800 |
| Impacto SJR |
1.17800 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069590641&doi=10.1016%2fj.neucom.2019.06.040&partnerID=40&md5=1bdddfc5ee18c8f890cb02e0bfd02b15 |
| Fecha de publicacion |
07/10/2019 |
| ISI |
000480413200020 |
| Scopus Id |
2-s2.0-85069590641 |
| DOI |
10.1016/j.neucom.2019.06.040 |
| Abstract |
Extreme Learning Machine (ELM) has proven to be an efficient and speedy algorithm for classification. In order to generalize the results of standard ELM, several ensemble meta-algorithms have been implemented. On this manuscript, we propose a hierarchical ensemble methodology that promotes diversity among the elements of an ensemble, explicitly through the loss function in the single-hidden-layer feed-forward network version of ELM. The diversity term in the loss function is justified using the concept of regularization from the Negative Correlation Learning framework. Statistical tests show that our proposal is competitive in both performance and diversity measures against bagging and boosting ensemble methodologies. (C) 2019 Elsevier B.V. All rights reserved. |
| Palabras clave |
Extreme Learning Machine; Ensemble; Hierarchy; Diversity; Negative Correlation |
| Miembros de la Universidad Loyola |
|
Gestionar cookies