| Título |
A Preliminary Study of Diversity in Extreme Learning Machines Ensembles |
| Autores |
PERALES GONZÁLEZ, CARLOS, CARBONERO RUZ, MARIANO, BECERRA ALONSO, DAVID, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS |
| Publicación externa |
No |
| Medio |
Lect. Notes Comput. Sci. |
| Alcance |
Proceedings Paper |
| Naturaleza |
Científica |
| Cuartil JCR |
4 |
| Cuartil SJR |
2 |
| Impacto SJR |
0.283 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048895231&doi=10.1007%2f978-3-319-92639-1_25&partnerID=40&md5=4eae5c44e664b66d50a7600b3e8b2061 |
| Fecha de publicacion |
01/01/2018 |
| ISI |
000443487900025 |
| Scopus Id |
2-s2.0-85048895231 |
| DOI |
10.1007/978-3-319-92639-1_25 |
| Abstract |
In this paper, the neural network version of Extreme Learning Machine (ELM) is used as a base learner for an ensemble meta algorithm which promotes diversity explicitly in the ELM loss function. The cost function proposed encourages orthogonality (scalar product) in the parameter space. Other ensemble-based meta-algorithms from AdaBoost family are used for comparison purposes. Both accuracy and diversity presented in our proposal are competitive, thus reinforcing the idea of introducing diversity explicitly. |
| Palabras clave |
Extreme learning machine; Diversity; Machine learning; Ensemble; AdaBoost |
| Miembros de la Universidad Loyola |
|
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